{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch_adaptive-grade"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch_adaptive-grade.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import sys\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.utils import shuffle\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n",
    "\n",
    "\n",
    "notebook.tqdm.pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainset saved.\n"
     ]
    },
    {
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       "model_id": "f578971da00e405f9d4a4012305b174b",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retention calculated.\n"
     ]
    },
    {
     "data": {
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       "model_id": "839bacc8f1f74989baa351c98b3c8a86",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.876        4.2    4.20       4176\n",
      "          1,3,3           8.6          0.883        9.2    2.19       2711\n",
      "        1,3,3,3          18.2          0.858       14.0    1.52       1616\n",
      "      1,3,3,3,3          37.5          0.835       23.2    1.66        822\n",
      "    1,3,3,3,3,3          78.8          0.850       35.6    1.53        384\n",
      "  1,3,3,3,3,3,3         122.3          0.903       39.3    1.10        171\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6589\n",
      "          3,3,3           9.0          0.960       23.7    1.56       5162\n",
      "        3,3,3,3          19.4          0.942       44.2    1.86       3519\n",
      "      3,3,3,3,3          39.1          0.926       54.5    1.23       1922\n",
      "    3,3,3,3,3,3          76.6          0.930      106.8    1.96       1074\n",
      "  3,3,3,3,3,3,3         118.7          0.949      155.3    1.45        480\n",
      "3,3,3,3,3,3,3,3         131.1          0.970      617.2    3.97        100\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       38.9    3.21       7517\n",
      "          4,3,3          18.0          0.963       56.8    1.46       5303\n",
      "        4,3,3,3          33.3          0.952       84.3    1.48       3012\n",
      "      4,3,3,3,3          48.3          0.953      128.3    1.52       1353\n",
      "    4,3,3,3,3,3          67.3          0.958      112.9    0.88        496\n",
      "  4,3,3,3,3,3,3          77.6          0.978      113.2    1.00        244\n",
      "4,3,3,3,3,3,3,3         112.9          0.984      177.8    1.57        168\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[df['type'] != 3].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "df['i'] = 1\n",
    "df['r_history'] = \"\"\n",
    "df['t_history'] = \"\"\n",
    "col_idx = {key: i for i, key in enumerate(df.columns)}\n",
    "\n",
    "\n",
    "# code from https://github.com/L-M-Sherlock/anki_revlog_analysis/blob/main/revlog_analysis.py\n",
    "def get_feature(x):\n",
    "    last_kind = None\n",
    "    for idx, log in enumerate(x.itertuples()):\n",
    "        if last_kind is not None and last_kind in (1, 2) and log.type == 0:\n",
    "            return x.iloc[:idx]\n",
    "        last_kind = log.type\n",
    "        if idx == 0:\n",
    "            if log.type != 0:\n",
    "                return x.iloc[:idx]\n",
    "            x.iloc[idx, col_idx['delta_t']] = 0\n",
    "        if idx == x.shape[0] - 1:\n",
    "            break\n",
    "        x.iloc[idx + 1, col_idx['i']] = x.iloc[idx, col_idx['i']] + 1\n",
    "        x.iloc[idx + 1, col_idx['t_history']] = f\"{x.iloc[idx, col_idx['t_history']]},{x.iloc[idx, col_idx['delta_t']]}\"\n",
    "        x.iloc[idx + 1, col_idx['r_history']] = f\"{x.iloc[idx, col_idx['r_history']]},{x.iloc[idx, col_idx['r']]}\"\n",
    "    return x\n",
    "\n",
    "df = df.groupby('cid', as_index=False, group_keys=False).progress_apply(get_feature)\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df[\"t_history\"] = df[\"t_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df[\"r_history\"] = df[\"r_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "def cal_retention(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group['retention'] = round(group['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x]).mean(), 4)\n",
    "    group['total_cnt'] = group.shape[0]\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history', 'delta_t'], group_keys=False).progress_apply(cal_retention)\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2, 0.2, 0.2, 1, 1, 2, 3, 4]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w, dtype=torch.float32))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        keys = torch.tensor([1, 2, 3, 4])\n",
    "        keys = keys.view(1, -1).expand(X[:, 1].long().size(0), -1)\n",
    "        index = (X[:, 1].long().unsqueeze(1) == keys).nonzero(as_tuple=True)\n",
    "        X_rating = X[:, 1].clone().float()\n",
    "        X_rating[index[0]] = self.w[13+index[1]]\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X_rating - self.w[13])\n",
    "            new_d = self.w[2] - self.w[3] * (X_rating - self.w[15])\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X_rating - self.w[15])\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.01, 0.2)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            w[13] = w[13].clamp(0.1, 10)\n",
    "            w[14] = w[14].clamp(0.1, 10)\n",
    "            w[15] = w[15].clamp(0.1, 10)\n",
    "            w[16] = w[16].clamp(0.1, 10)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from [self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist()]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=1, lr=1e-2, batch_size=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        self.eval()\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader))\n",
    "\n",
    "        for i, batch in enumerate(self.pre_train_data_loader):\n",
    "            self.model.train()\n",
    "            self.optimizer.zero_grad()\n",
    "            sequences, delta_ts, labels, seq_lens = batch\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences)\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "            self.model.apply(self.clipper)\n",
    "            pbar.update(n=real_batch_size)\n",
    "\n",
    "        pbar.close()\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "\n",
    "\n",
    "        for k in range(self.n_epoch):\n",
    "            self.eval()\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "                \n",
    "        self.eval()\n",
    "\n",
    "        w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "        return w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sampler = RevlogSampler(self.train_set, batch_size=4096)\n",
    "            train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "            loss = 0\n",
    "            for batch in train_data_loader:\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss += self.loss_fn(retentions, labels)\n",
    "            self.avg_train_losses.append(loss/len(train_data_loader))\n",
    "            print(f\"Loss in trainset: {self.avg_train_losses[-1]:.4f}\")\n",
    "\n",
    "            sampler = RevlogSampler(self.test_set, batch_size=4096)\n",
    "            test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "            loss = 0\n",
    "            for batch in test_data_loader:\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss += self.loss_fn(retentions, labels)\n",
    "            self.avg_eval_losses.append(loss/len(test_data_loader))\n",
    "            print(f\"Loss in testset: {self.avg_eval_losses[-1]:.4f}\")\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22cef8b7204b401a8ee14f320ef7ef2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/223795 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/fsrs4anki/lib/python3.9/site-packages/sklearn/model_selection/_split.py:909: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 147736 TEST: 76059\n",
      "dataset built\n",
      "Loss in trainset: 0.3519\n",
      "Loss in testset: 0.3179\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e6f3c9a1e39482d958783be462239ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/17371 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.9574, 1.4838, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0, 0.5746, 1.2134, 3.5173, 4.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51b8a36a514b4443a526d1f756dac8c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/391095 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3460\n",
      "Loss in testset: 0.3094\n",
      "iteration: 39168\n",
      "w: [1.037, 1.4547, 4.9983, 1.0557, 0.9079, 0.0523, 1.3826, 0.1113, 0.9474, 1.7542, 0.5092, 0.5032, 1.274, 0.2648, 0.5308, 3.5411, 4.9877]\n",
      "iteration: 78336\n",
      "w: [1.037, 1.4547, 4.7403, 0.9054, 1.0268, 0.0356, 1.3515, 0.1075, 0.9522, 1.8452, 0.4836, 0.5927, 1.4488, 0.1, 0.3292, 3.7813, 4.9907]\n",
      "iteration: 117504\n",
      "w: [1.037, 1.4547, 4.5552, 0.8628, 0.9533, 0.0354, 1.3248, 0.1251, 0.9654, 1.826, 0.5239, 0.497, 1.543, 0.2678, 0.3166, 3.7175, 5.2319]\n",
      "Loss in trainset: 0.3296\n",
      "Loss in testset: 0.2915\n",
      "iteration: 156477\n",
      "w: [1.037, 1.4547, 4.4207, 1.0703, 0.6827, 0.0202, 1.347, 0.1471, 1.0163, 1.8732, 0.459, 0.5346, 1.3595, 0.1, 0.3915, 3.7654, 5.3806]\n",
      "iteration: 195645\n",
      "w: [1.037, 1.4547, 4.3876, 0.9399, 0.9199, 0.0276, 1.2619, 0.0911, 0.9426, 1.7442, 0.5979, 0.6101, 1.3685, 0.1, 0.2709, 3.9356, 5.3166]\n",
      "iteration: 234813\n",
      "w: [1.037, 1.4547, 4.2197, 0.7149, 0.6936, 0.0, 1.2012, 0.1182, 0.8915, 1.7921, 0.5467, 0.552, 1.4506, 0.1684, 0.625, 3.7844, 5.4404]\n",
      "Loss in trainset: 0.3288\n",
      "Loss in testset: 0.2903\n",
      "iteration: 273786\n",
      "w: [1.037, 1.4547, 4.2034, 0.7457, 0.673, 0.0398, 1.2152, 0.0442, 0.9012, 1.8014, 0.5253, 0.6461, 1.3748, 0.1082, 0.5763, 3.8669, 5.3815]\n",
      "iteration: 312954\n",
      "w: [1.037, 1.4547, 4.1723, 0.6925, 0.6712, 0.0243, 1.195, 0.0446, 0.8771, 1.7661, 0.5516, 0.5718, 1.3382, 0.1166, 0.6063, 3.8894, 5.3763]\n",
      "iteration: 352122\n",
      "w: [1.037, 1.4547, 4.1753, 0.7194, 0.6698, 0.0176, 1.1866, 0.04, 0.865, 1.7818, 0.533, 0.6203, 1.3391, 0.1193, 0.597, 3.8838, 5.409]\n",
      "iteration: 391290\n",
      "w: [1.037, 1.4547, 4.1791, 0.7244, 0.6776, 0.0096, 1.1852, 0.0436, 0.8633, 1.7898, 0.5241, 0.6225, 1.3446, 0.1134, 0.5878, 3.8874, 5.4163]\n",
      "Loss in trainset: 0.3286\n",
      "Loss in testset: 0.2901\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 149817 TEST: 73978\n",
      "dataset built\n",
      "Loss in trainset: 0.3430\n",
      "Loss in testset: 0.3349\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ac389d307ea4614b49671e9dfc545af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/19836 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [0.9, 2.1357, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0, 0.1589, 1.0939, 3.0, 5.2186]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f3655aea5eb4773bc927c31c37a2a47",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/389943 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3350\n",
      "Loss in testset: 0.3290\n",
      "iteration: 38912\n",
      "w: [0.7618, 2.0581, 4.966, 0.752, 0.6041, 0.0328, 1.3493, 0.0543, 0.9514, 1.7586, 0.5073, 0.4587, 1.0447, 0.1, 0.7574, 3.3038, 5.1223]\n",
      "iteration: 77824\n",
      "w: [0.7618, 2.0581, 4.7871, 0.7906, 0.5984, 0.0, 1.3084, 0.0613, 0.9823, 1.8234, 0.4745, 0.5854, 1.2201, 0.314, 0.3827, 3.466, 5.2709]\n",
      "iteration: 116736\n",
      "w: [0.7618, 2.0581, 4.6166, 0.5869, 0.7727, 0.0394, 1.0972, 0.1277, 0.8337, 1.863, 0.466, 0.5266, 1.233, 0.1116, 0.8385, 3.6437, 5.5345]\n",
      "Loss in trainset: 0.3182\n",
      "Loss in testset: 0.3116\n",
      "iteration: 155581\n",
      "w: [0.7618, 2.0581, 4.3941, 0.5425, 0.7699, 0.025, 1.104, 0.0605, 0.8999, 1.8684, 0.4474, 0.5294, 1.5409, 0.1932, 0.9818, 3.8185, 5.5957]\n",
      "iteration: 194493\n",
      "w: [0.7618, 2.0581, 4.3021, 0.5164, 0.6825, 0.0364, 0.9906, 0.0392, 0.8006, 1.8345, 0.4815, 0.6889, 1.3374, 0.141, 0.8046, 3.8642, 5.7555]\n",
      "iteration: 233405\n",
      "w: [0.7618, 2.0581, 4.2931, 0.5963, 0.6605, 0.0, 1.0677, 0.0854, 0.8831, 1.8442, 0.4702, 0.568, 1.2816, 0.1, 0.7078, 3.9613, 5.9025]\n",
      "Loss in trainset: 0.3181\n",
      "Loss in testset: 0.3116\n",
      "iteration: 272250\n",
      "w: [0.7618, 2.0581, 4.2833, 0.6239, 0.6722, 0.0265, 1.0755, 0.0399, 0.8981, 1.8496, 0.4591, 0.585, 1.1995, 0.1339, 0.6139, 3.9763, 6.0052]\n",
      "iteration: 311162\n",
      "w: [0.7618, 2.0581, 4.2734, 0.6694, 0.5764, 0.0069, 1.0381, 0.0526, 0.8638, 1.8191, 0.491, 0.6222, 1.1606, 0.1018, 0.5863, 4.0288, 5.9784]\n",
      "iteration: 350074\n",
      "w: [0.7618, 2.0581, 4.2677, 0.6369, 0.6121, 0.0195, 1.0589, 0.0312, 0.8837, 1.8058, 0.4995, 0.6022, 1.1375, 0.1017, 0.5896, 4.0461, 5.9739]\n",
      "iteration: 388986\n",
      "w: [0.7618, 2.0581, 4.2621, 0.6264, 0.6089, 0.0174, 1.0587, 0.0339, 0.8834, 1.81, 0.4943, 0.6045, 1.1436, 0.104, 0.596, 4.0403, 5.979]\n",
      "Loss in trainset: 0.3177\n",
      "Loss in testset: 0.3112\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 150037 TEST: 73758\n",
      "dataset built\n",
      "Loss in trainset: 0.3263\n",
      "Loss in testset: 0.3690\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4715c7d4d16f442bbbc12bfcdecffc31",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/20733 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.7023, 1.8571, 5.0, 0.5, 0.5, 0.2, 1.4, 0.12, 0.8, 2.0, 0.2, 0.2, 1.0, 0.2615, 2.0, 3.0468, 5.1835]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7d8fa1caf5049ceb24e372de8c058d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/387912 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3202\n",
      "Loss in testset: 0.3683\n",
      "iteration: 38656\n",
      "w: [1.8305, 2.0011, 5.2173, 0.9473, 0.7879, 0.038, 1.292, 0.1787, 0.8976, 1.8047, 0.5639, 0.6606, 1.3964, 0.1272, 0.2893, 3.5351, 5.5105]\n",
      "iteration: 77312\n",
      "w: [1.8305, 2.0011, 4.954, 0.9364, 0.8416, 0.1003, 1.2287, 0.0192, 0.8524, 1.8475, 0.5364, 0.686, 1.6863, 0.1008, 0.182, 3.845, 5.605]\n",
      "iteration: 115968\n",
      "w: [1.8305, 2.0011, 4.6566, 0.6596, 0.9314, 0.0976, 1.2883, 0.01, 0.9412, 1.9179, 0.506, 0.5546, 1.6636, 0.207, 0.1023, 4.0076, 5.5264]\n",
      "Loss in trainset: 0.3021\n",
      "Loss in testset: 0.3515\n",
      "iteration: 154392\n",
      "w: [1.8305, 2.0011, 4.645, 0.859, 0.8236, 0.1072, 1.1827, 0.1115, 0.8859, 1.9466, 0.5028, 0.5772, 1.818, 0.1, 0.1, 4.0744, 5.854]\n",
      "iteration: 193048\n",
      "w: [1.8305, 2.0011, 4.6006, 0.9191, 0.7796, 0.0627, 1.1742, 0.01, 0.8883, 1.8402, 0.6086, 0.6378, 1.7044, 0.1115, 0.1, 4.0523, 6.0259]\n",
      "iteration: 231704\n",
      "w: [1.8305, 2.0011, 4.541, 0.7172, 0.8305, 0.0695, 1.1513, 0.0273, 0.8708, 1.8655, 0.5769, 0.6075, 1.6989, 0.151, 0.1104, 4.1154, 5.9283]\n",
      "Loss in trainset: 0.3021\n",
      "Loss in testset: 0.3513\n",
      "iteration: 270128\n",
      "w: [1.8305, 2.0011, 4.5379, 0.7115, 0.7901, 0.0446, 1.0888, 0.01, 0.8064, 1.8257, 0.611, 0.5822, 1.5895, 0.1, 0.1, 4.2331, 5.9083]\n",
      "iteration: 308784\n",
      "w: [1.8305, 2.0011, 4.4985, 0.7223, 0.7775, 0.0403, 1.1257, 0.01, 0.8362, 1.8714, 0.5616, 0.6445, 1.6064, 0.1031, 0.1078, 4.2437, 5.9685]\n",
      "iteration: 347440\n",
      "w: [1.8305, 2.0011, 4.4798, 0.6932, 0.7746, 0.0255, 1.1327, 0.017, 0.8419, 1.8656, 0.5661, 0.6558, 1.5936, 0.1422, 0.1, 4.2305, 5.9689]\n",
      "iteration: 386096\n",
      "w: [1.8305, 2.0011, 4.4811, 0.693, 0.7753, 0.0271, 1.1322, 0.0156, 0.8411, 1.8709, 0.5605, 0.6546, 1.5965, 0.1366, 0.1015, 4.2362, 5.967]\n",
      "Loss in trainset: 0.3008\n",
      "Loss in testset: 0.3500\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=3)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=3, lr=4e-2, batch_size=256)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.2098, 1.838, 4.3074, 0.6813, 0.6873, 0.018, 1.1254, 0.031, 0.8626, 1.8236, 0.5263, 0.6272, 1.3616, 0.118, 0.4284, 4.0546, 5.7874]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,1,2,5,10,21,43,89,182,370,748\n",
      "difficulty history: 0,7.0,6.9,6.9,6.8,6.8,6.8,6.7,6.7,6.6,6.6\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,2,4,9,19,40,84,175,362,743,1515\n",
      "difficulty history: 0,6.8,6.7,6.7,6.6,6.6,6.6,6.5,6.5,6.4,6.4\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,8,22,59,156,404,1027,2566,6304,15234,36231\n",
      "difficulty history: 0,4.3,4.3,4.3,4.3,4.3,4.3,4.3,4.3,4.3,4.3\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0,12,36,106,305,857,2357,6343,16723,43212,109517\n",
      "difficulty history: 0,3.1,3.1,3.2,3.2,3.2,3.2,3.2,3.3,3.3,3.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "        stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "        return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(f\"interval history: {t_history}\")\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([8.4453, 4.3074])\n",
      "tensor([22.3648,  4.3074])\n",
      "tensor([59.4913,  4.3074])\n",
      "tensor([156.0754,   4.3074])\n",
      "tensor([403.9080,   4.3074])\n",
      "tensor([32.4436,  6.9643])\n",
      "tensor([5.6330, 9.5734])\n",
      "tensor([8.0332, 9.4786])\n",
      "tensor([11.3945,  9.3856])\n",
      "tensor([16.2269,  9.2941])\n",
      "tensor([23.5435,  9.2044])\n",
      "tensor([34.9212,  9.1162])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,8,22,59,156,404,32,6,8,11,16,24,35\n",
      "difficulty history: 0,4.3,4.3,4.3,4.3,4.3,7.0,9.6,9.5,9.4,9.3,9.2,9.1\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.012427\n",
      "2     0.036426\n",
      "3     0.145052\n",
      "4     0.133421\n",
      "5     0.012324\n",
      "6     0.051717\n",
      "7     0.144637\n",
      "8     0.032087\n",
      "9     0.097268\n",
      "10    0.334641\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(10)\n",
    "for i in range(10):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  100.18\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5eb5e5cae2874d72a6c0d0394bec9e73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.82-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "index_len = 664\n",
    "index_offset = 200\n",
    "d_range = 10\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 200000\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return int(round(np.log(stability) / np.log(base)) + index_offset)\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + avg_w[15] - avg_w[13]) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * (11 - d) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * np.power(s, avg_w[11]) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2)):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        while diff > 0.1:\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            for s_index in range(index_len - 2, -1, -1):\n",
    "                stability = stability_list[s_index]\n",
    "                interval = max(1, round(stability * np.log(recall) / np.log(0.9)))\n",
    "                p_recall = np.power(0.9, interval / stability)\n",
    "                recall_s = cal_next_recall_stability(stability, p_recall, d, 1)\n",
    "                forget_d = min(d + d_offset, 10)\n",
    "                forget_s = cal_next_recall_stability(stability, p_recall, forget_d, 0)\n",
    "                recall_s_index = min(stability2index(recall_s), index_len - 1)\n",
    "                forget_s_index = min(max(stability2index(forget_s), 0), index_len - 1)\n",
    "                recall_time = time_list[d - 1][recall_s_index] + r_time\n",
    "                forget_time = time_list[forget_d - 1][forget_s_index] + f_time\n",
    "                exp_time = p_recall * recall_time + (1.0 - p_recall) * forget_time\n",
    "                if exp_time < time_list[d - 1][s_index]:\n",
    "                    time_list[d - 1][s_index] = exp_time\n",
    "            diff = s0_time - time_list[d - 1][s0_index]\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(10)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3403\n",
      "Loss after training: 0.3154\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9124\n",
      "RMSE: 0.0191\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = np.sqrt(mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts))\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration')\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration')\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0dcfVw+tuV0MIO5kmMydjUzkek8zJy6mcuWINFs/Gp5FpshT4uvp+brzZuzGdG1RFUfJZGGPKhONr4OIBW9BIwlnIyjstSgc0AuuuzqnXHsWhdwG9s/WjwRWa9oUHxqNqdLzz81H+tysaRYEPn2jJY62qAeB9bf5iQtrtB5G5vZkyJ7JiuevD2bdiNBqZM2cOc+bMKTBPrVq18gxX36xz584cOHCg0DyjR4+W4WshhKhkzBaVs1fTOBGTwonYFOvHmBTOXE2joB13tBqF6t7O1PRxobavK7V8rR9rV3GhThU3NPmtqr5yCvYtgshl1l7H/Lj5g3dt8KoF3rXAuzZmV38m/XSUs/EZdG3kx7D76wAKKMpNHzXXA0VbwOgCOuO1PPZUVeW9X46xeOcZAKb3bUG/ttfXJfi4WnsNE67NZywui0WVYw8rqDKxsEYIIYQobWaLyrn4dE5dTuXk5VROXU7l79gUTl5OKbBn0dtFT8MAdxr6u1OnqpstWKzm7YxeW4T5iTnZcOIX2Lvw+iIWAM8a0Ohh8KljCxbxqmkN/m6iBR7t24Yn5+1i5zEIeagNjQOLMEReCFVV+WDdcRbsiALg/cebM+CeGnZ5vK8NPcffZk9kanaOLRiXILJikSBSCCFEhZJjthB1Jc0WKOZ+PB2XSnZO/sGik05DA393Gga40yjA+rFhgDtV3ZzyH5K+lYQzsG8JHPga0uKuJSrQIAzaDYN6oQ6dQd2+ji+9mgew9q8Ypv58lOUj2hevXlgDyI82nODLbf8A8O5jTXmqfc08+XKDyNtdWJN0rSfTqNfc9n6TomyRIFIIUbaoFlqcXUyLmO/ZU30of1fvd7drJMqRnaevMGHVIS4kZuR73UmnoU5VN+r7uVHPz/qxYYA7tXxdb29jb3OOdW5jzCE4sAxO/YZt8YtbALR51vrwqlFoMYWZ2LMxvx27TMQ/V1l/JJYezfLfgu5WPv3tJHO2nAZg8iNNeCakdr75vK8NZ8ff5nB27vY+stF4xSNBpBDi9qlqvnOuHOWSfZVux9+mXsqfAPQ++wGKRsuJoD63Xbao2LJzLMzc+Ddfbj+NqoKLQXstUHSnvr8b9aq6Ud/fjereLsUPFlXV2qt49RRcOWn9mPt5QhRYcuzz1+li7XVs2BO0tz+MW8PHhZEP1GH2llP8Z+1ROjes6lDPnqqqzNz4N//dfAqAt3o3ZmjH4ALzl9TCmsQM6+tlKLvikSBSCHF7TBmwsAekXoYHX4PWz4DW8V8tNRN30/3EJNxz4slQDfxhaUqo9gA9ot4HnRP7jSGlUHlREZyOS+WVFQc4fMG6unngPTWY9EgTXAy38ScuJ9u6n+LFA3Ax0trDeOUUZCUV/BqdM/jWhbpdoe1z1s9L2KjOdVm59xzn4jNY+EcUL3WuV6TXqarKu2uOsfAP6xzIiT0b8fwDdQp9jbdtYc1tDmfnLqqRldkVjgSRQojbEzEHLkVaP18zFnZ/AQ9Nhfrdi9Y7acmhc8xCOsStQIPKcUsN3tKPJ6BOS2KOv8/T2t/ocXIKGTXfBIr2B1NUDqqqsuLPc0z9+SgZJjNeLno+6NucHs0CHSvIbILL1wLGS5HWj7FHrEcB5qFYF8D41oMq9a0fcx8e1aCUN8N3ddLxRs9GjF95kDmbT/FEm+r4eRS+ibnFovLv1Yf5vz3RALzzaFOGdKh9y3v5XJsTeds9kemy0XhFJUGkEKL4ki/B7zOtn7ccBH+vg7jjsHwABHeCh96FoFYFvz7xHO7fPcf9cXsBWJ7TlS+dR9CjdTBGvZat2a+hP53Dk7qtPBY9jYx/aoLPk7dV5ewc6zFuV1KziE/LJj4tm6tp2cSnZV1Lz8bX1cC/whpSxc3p1gWKuyI+LZs3vjvEhqPW42o71vPl4/6t8p4Kk7uJd0oMpMZae8xTY659jIX4fyDmcP4bcxs9Iag1BLaCwJZQtZF1NbX+9o4AvF19WlVjScRZDp5L5MP1J/iwf8sC8+aYLUz49hA/HLiARoEP+rVgQLuizcvMXViTlm0mK8eMk654i2KS5NzsCkuCSCFE8W1+F0xpnHFuypSEobzw8Gvcd2Exyu4vrNuZzHsQWgyErm/lXVBw/BdY/RL6zERSVGfeMI3goEcXHmkZZDvarWl1b/4vZTyGOBOPa//AdW04ePpC/VCHqnk5JZP3V+3g3Nl/uJTlRCJupOMEFNxTevRSMv834j67c45F2fD7yTheXXmQyylZ6LUKr4U1Yvh9gWhiD8HRvXBhL8RHXQ8aLUVYGOLkCUEtrweNQa2t2+6UwFzfkqbRKEx+pAl9P9/Jqn3neSakFi2qe+XJl51j4eX/O8C6IzHoNIrtJJqicjfq0GoUzBaVxHQT/h63F0TKnMiKR347CiGK5+IB66bJwNjEJ4lMuMLWv6/QumYYE3o/TkjU5yh/rYRDK+DIDxDyEtw/zrrx8Ya3Yc+XAByy1CHcNAatTzCPNQ9Ed9Pee/c3DOCTtHEY0k30Zg/qN4NRnvoG6nQuUjUPHdxL1Or3mWHZikExw7VOpBy0pCpupGvdydK5k6P3xGz0QjV6sva8kfnnH+TF/+1jwZB7HDuvWDjkXHw6G47GYtBp8HTW4+Wsx8tFf+1zA+5GnW3j7qwcMx+uO8H8Hf9QS4llpNc5XqibgO/xD2HrX4UHi87e1lXSbn7WDb3d/a0fPapZexm9g0t9KLoktanpzeOtq/HDgQtM/fkoq14MsdvyJ9Nk5sX/7WPriTgMWg1zBrfhoSaOncCm0Sh4u+i5kmrtsfe/xbB5Qa6fViOrsysaCSKFEI5TVfj1DQB+MN9PpFqPh5r4s/3vOA5EJ/JUdCItqg/hzW4DaH/6U5QzO2DHJ7B/qfUP9+WjAMzP6cX0nIFU93ald4ugfFfNajUKYS2q8ebulzGYP+Eh9qH+3yCUp7+DWh0KruKFA5z58T2axW6ihaKCAjlO3mhNqSgWEzrMeKlJeOUkQQ6QCaRYX9sYeNxpIy+feonXvjUwc0Cr/E8gEcV2Lj6dOVtO8e2+8+TYHQuj4kYG3koK3qTiraQSZMgg0JCOuzmRDtmneMnpFD5KqvU9O3LDS12rQrV2UL0t+DWxBo3u/tZ0XcWbmvB6j0asOxzD3rMJ/HzoEo9e62VMy8rh+SV7ifjnKka9hq+ebccD9asW6x5eLgaupGbf1rzI3DmR0hNZ8UgQKYRw3JEf4NwuMjEw3fQkj7euxidPtuJySiZfbf+H/+2K5tD5JAaeh0b+r/FuyFO0O/kpypW/If0qmXovRqWNYIulNY81r4qvk1rotisuBh1d6vkw/u+x/Nf8EZ05CMv6wzOrocY91zOqKkRtx/z7TLRRWwkGUOAv1w7U6/s2znU7WPOY0iEjETITrfPlbJ9fex65nDopF/nBMJlP/nqC6e4vM7F3s1L8ghbN2atpbDway4ajsZyISUGrUdDlPrQadNrc5xr0WgWtRsGo11LPz42mQR40DfKkgb970XpWLRbIToHMZOuZzplJ1z/PSrF+DbPTwZR27WPGDZ+nQ3batYUp147lUzSgKGSZITbFOve0HwqP6zR4OmnwIA0XczJulmR0mPPWJzeGyR1R1RqsPYjV74FqbaF6O+tRgWVw+Lm0BHgaGdW5LjM3/s20tcd4qLE/2WYLQxftYX90Im5OOhY+dw/3BvsU+x62xTW3sVdkosyJrLAkiBRCOMaUARsnAzDX9Ag67+q881hTAPzcjfy7dxNefLAuC3ZEsTTiLMdjU+kf60XDqtOZ3uog2suHGfHPg8Tgy9COtQnvEMAXW0/f8rZVXPVM7NGIF9aMYxEz6JB9FP7XD4b8BAEtrEfL7fgELuxDC+SoGtZYOmDpOJbHw0KvD/UpChhcrQ/PavnfLCQc1oxFf/RHXtN/w+7dB1mh/5iB3e8via+gvcxkSL4ASRcg+bw1iM22BmNqdhqJSQlcTUggOTkZslPpSBbdycRJMaFYQEEFVKynJ1t79JSbnpvPazDv15KDhlh0aLQ6dHo9Br0BJ4MBJycDWq3OGgBmJV8PFlELqnWxOQE1gZo3xrH5xSc6Z1Rnb3KM3pgMXmTqvcjQeeJdqxkude4D/+agk+HRkZ3q8M2f57iQmMGM9cfZExXPkYvJeDrrWTrsXlrW8Lqt8r1drYFf/G1s85MscyIrLAkihRCOiZgNSdFcVH2Yrz7C0oGt8DDa/3HwdXPitR6NGNmpDov+OMPCP6I4EZdBn7gGQAMAXu5Wn3Gh9UlISCjyrXs0qcLZ5EYM3/4vvtZMp13WCfi6j3W48srfAGSqelaYu/C98XHeGtyjeL0wLj7QfwlELid7zb9oz3Ea//EE+7Km0PaRFx0ry2yC83utq4CTL0DS+RuCxgvXgrX8KYD3tQcAJTVlzwJkXXvcitYATh5g9LCuVnbyACd30LuAwQX0rtc+ulgD8xvTdQbiUjJZc/ACO07GYbFY0GChaaA7j7QIpH5VF1CvHUPo7AXOPtavvbMPGFxQAP21h0sJNb2iMeq1TOzViNHLD7DojzMAVHEz8PXw9rd9vjbccPRhCQxny4k1FY8EkUKIoku+hOX3mWiAD0yDGNG1KW1rFRykebkYGPdQA4Y/EMzSnWeYvyOKxHQTb/ZqxMhOxduI+bUejTgWk8Jzf0/gG+fpNM04CRkJZGrd+CorlMU5YdSpXZv5T7W55f55hVIUaD0Yfa0OnFvwNDXSDtN23+tcvrIDv4GzrUFPQVJi4dRGOLkBy6nNaLJTCr1VmsaNq9qqXNFU4VyWC4k5BtIxkq46kaN1prp/VRrU8KdxrUDc3DyswZrOyTpEnDtcfOPH3Prnfq6awWJGtZi4nJhO1OUkzsQlEX0lhXNXkknJyESHhUwMpKjOGN28ua9JHbq2qkeL2v4On9GcaTJzPCaFlXvPsWrvOUzmICCI++tV4ZXQ+txTu/jDqyKv3s0DWVr7LHvOxBPgYWTZiPbUrepWImVfP/rwNoLIjNyFNdITWdFIECmEKDLLpqloTOnst9TjQvXezOxStM2/PYx6Rnetz7D7g7mamk0Nn+L3K2k1Cv8d2JrH5qQx6OprzPD5iXh9IO/H3ksqLgztWJs3ezVGry2ZbjvFJ5hq47ayZu5r9LiyGL+zP5M9ex+G/l9B7WvD2xaLdbX6yfVwcoP182s0wFXVnSOW2lxSfbmEDxdVXy6pvraP6dgHu1XcDIQ29qd7U3861K3i0NF2hbYF8A8C/yZw3w3pl1MyOXguiV8PXyLySCypKTns3p3ErN37qOHjzMMtgni4RSBNAj3yBJRZOWZOxKRw6HwShy8kceh8En/HptgtlulYz5dXujW4rbl5omCKojD7qdas3HuOx9tUp5qXc4mVfbtHH2aazGSarL3NcmJNxSNBpBCiaC7sR3NwOQAfKUP5dGDrPNvx3IqLQYeLz+3/2vF00fPVs+3oM+cPXowfBICzXsusfs15rFUB8xxvg0an56FRHzH1y5YMjX2f4LSLqIsfRrnnechOhZMbIf2K3WsOWYLZYmnNFnMrDDXa0KyGL3qdgkGrobpWQ7DWuvjFSadBr7320Gmo5uVMqxpexT/fuRj83I081MTIQ038yTSZ2fZ3HGsOXeK3o7Gci89g7tbTzN16mjpVXHm4ZRD+Hk4cvpDEXxeSOBGTgsmcd+6kr6uBNrW8GfFAHQke7wA/DyOju9Yv8XK9b3NhTe58SI0CbrdzDKUok+QdFULcmqqS+uME3IDvzfczoO/jt9WbWBLq+7vzyZOteGnZfqp7O/PFM21pFHD7c8AK4qTTMmH4YJ77Moj+cXMYqNsKf35lu56hcWFbTjM2mVux1dySTGNV+rWrzvR7a9IwwL3U6lXSjHotYU0DCGsaQHp2DpuPX+bngxfZciKOf66k8dmmk3le4+2ip1k1T1pU96R5NS+aV/ckyNPo8DC4KHt8bvP87MQbFtXINlkVjwSRQohbyoj8FrfLe0lXnTjY4GXeaV3yvX3F0b1pABETu+Hloi+x4evCuBv1zB3WiX5zdWxObM1zLn9wXgnih7Sm7LU0xISOVjW8mNC+Jo+0CMLZUDLD0HeLi0F3bSg7iJRMExuPxvLr4RgyTWaaBuUGjZ5U93aWgLGCyt0gPL6Yw9m2RTWy0XiFJEGkEHeYqqr8HZvK7qirVPNy5sEGVR0eFr6jTBlk/PJvnIHl+sd5tX/Xu10jO1Xd7+wm0n7uRpYOa88Tc81sSLXuUelq0DKgdTWeal+TpkGed7Q+d4q7UU/fNtXp26b63a6KuINyeyITizmcnXtajWzvUzFJECnEHZCWlcMfp66w5UQcW09c5lJSpu2an7sT/dtVZ0C7GtTydb2Ltczfse/fp3FOLBdVH9oMnJRnO5/KKLiKK0uH38sX2/4hpI4vj7YKwk3O2BYVUO5m46lZOWTnWBw+AlTOza7Y5LeeEKVAVVVOx6Wx9cRltpy4zJ9RCWSbLbbrzjp4LuAfEq5e4Wqahr+26flzq4H61arStXkt7m9UDSdnV+s50zoj6J3vykkcF6L/odbRL0GByIbj6FWvbAxjlwVNgzz576DWd7saQpQqd6MOjQIW1dqr6Oi2WUlyWk2FJkGkECUo+mo6C3b8w+YTlzkXn2F3raaPC90beNJfv4P6pxaiuRJlvXDjVKErwJZrjxsZ3KBqI/BrbD0T+NrHLKMviRk5xKdZz7bNMJlxN+rxcNZZPxp1uDnpbjlfLcdsISHdRHxaNlfTskhIMxGfmkG1bf+iq5LFCV0jug8Iv90vjxCinNFoFLxdDFxNyya+GEHk9Y3GJYisiCSIFJXaldQssnMsBN3mvmqZJjNfbDvN51tPk51j7XE0aDW0r+ND54Z+dA12pnbUNyi7PofUGOuLnL3Bv5n1GMGcTEzZGWSkp2LOykCvZmMkG51yrfcyOxUu7LU+bpCuunFGrc4JSw3+VqtzWg0iWXUlGWdSVBdScEFVtLbA0sOox92ow9WgIznTxNXULLRpsfhknaeWEkOwEkNtJYY6SgxdlViclWubBPf9GJ2ufC8SEUIUj7frtSCyGItrcjca95SFNRWSBJGi0jpyMYlB83aRkpVDz2YBvNS5Hs2qOb4oYvPxWKb8dJTo+HTAurHy0A7BhNT1xTUnEXZ/AV/Pg8wk6ws8qkGHMdDmWevJI9fkHu9mtqj8ceoK3+w9x6YjF9CaswhUrtJQOU8DzXkaKOdooJynthKDt5JKe+U47TXHC6xfuupEisWZlFQX60N1JgsD1ZQr1FJicFWy7HtDb2BGy5XWY/BvUgpnRgshyoXcDceLs7gmKSMHkDmRFZUEkaJSOh2XyrML9pCcaf0Ft/avGNb+FcODDaoS3qVekTZHPhefztQ1R9l4NBaAAA8jbz/chF7NA1CSzsGmN2H/Usi5NqxdpQF0HAvN+4Ou4P/KtRqFTg2q0qlBVeLTmvFj5AVikjLxdjXg42JA52og0VXPOScLvhlncU36G03ccbh8DBLOWM9izkwGUxoALkoWLmThryTmez9V0WDxqI7Gtx6Kb13wrQs+1o9ar5r4a+WXvxCVmfdtbPOTuzpbhrMrJgkiRaVzITGDZ+bv5mpaNk2DPJj6WFO+jjjLTwcvsu3vOLb9HUe7Wt6Ed6lH54ZV88wnzDSZ+Wr7P8zecoqsHAs6jcLw+4N5uUswrjF7YPU78NcqsFgDVIJaw/3jodHDoHFsZaOPq4GhHYMLyVEFaJv/JbMJslKsPaC5geWNAaZHNfCpi+JdC63uzm6TI4QoP2yn1hQjiJSFNRWbBJGiUolLyeLp+bu5mJRJ3aquLB12L75uTrSt5cP4hxry5fbTrNp7nr1nExi6+E8aB3oQ3qUuPZsFotUobD1xmSk/HeHMVevQ9f3BHkxvHU+1mM/hv79A+tXrN6vTGe4fB8EP3pWV1Wj14OJjfQghRDF5uxb/6MPrm41LEFkRSRApKo2kdBPPLNhN1JU0qnk587/n2+Prdr0HrqavC/95vDkvd6vPgh1R/G/XWY5dSmb08gMEV/mb2r4ubDkRhxPZ9Hc9ysuBx6getw1lbfL1mzj7QKNe0G4YVCugh1AIIcoRH1drAFicow9ln8iKTYJIUSmkZeXw3OI9HI9Joaq7E8ueb0+gpzPE/Q0JUaBorL2FigZ/RcObjTWMqePEuiOx/HI4luSrFtzir/C54U9CdQcxmDPh/LXC3QKg8cPQ+FGo1RG08mMlhKg4inv0odmikpyZG0TK6uyKSP7aiQov02RmxNK9HIhOxMtFz/LBDah9ehl8vxwuHijwde5A/2sPbpwyaAE8a0KTR6HxI1D9XofnOgohRHmRe2pNooM9kSmZJlTV+rn0RFZMEkSKCs1ktjB6+QH+PB3Lw4ZDvFfjL7yWbgbLtbk9Gp11825FAdUCKtc+3vBAtX7Uu0L9h6zBY2CruzPPUQgh7rDcOZHxDgaRufMhXQ1ah49LFOWDBJGiwrKYLcz6ehUdT33HDKed+CgpcPbaxYAW0Oop63Y7rlXuaj2FEKIss+0TmebYwhqZD1nxSRApKh6LBfXP+VzeMpd/Zf5z/bvc1Q9aDLAGj/5N72oVhRCivPC51hOZkpVDdo6lyL2KiblBpJxWU2FJECkqnq3vo2z/kAAgS9VzpXo3qj04HOp2lUUvQgjhIA+jHo0CFtU6L7Ko52fLRuMV312dpLB9+3YeeeQRgoKCUBSF1atX211XFCXfx4cffmjLU7t27TzXP/jgA7tyDh06xAMPPIDRaKRGjRrMmDEjT11WrVpFo0aNMBqNNG/enLVr15ZKm0UpO7Iatlu/Pz40DeDn7luoNuIbaNBdAkghhCgGjUaxrdB2ZK9IGc6u+O5qEJmWlkbLli2ZM2dOvtcvXbpk91i4cCGKotCvXz+7fFOnTrXLN2bMGNu15ORkunfvTq1atdi3bx8ffvghU6ZMYd68ebY8O3fuZNCgQQwfPpwDBw7Qp08f+vTpw+HDh0un4aJ0xByG1aMAmJ/Tk9R7X+GJjs3vcqWEEKL8y50X6cg2P0my0XiFd1e7Znr27EnPnj0LvB4QEGD3/Mcff6RLly7UqVPHLt3d3T1P3lzLli0jOzubhQsXYjAYaNq0KZGRkcycOZORI0cCMGvWLHr06MGECRMAePfdd9m4cSOzZ8/miy++uJ0mijsl7SqsGASmdH43N+MjdTBbO9e727USQogKwXr0YZpDG45fnxMpQWRFVW7W3MfGxvLLL78wfPjwPNc++OADfH19ad26NR9++CE5OTm2axEREXTq1AmD4frE3rCwME6cOEFCQoItT2hoqF2ZYWFhREREFFifrKwskpOTbY+UlJTbbaIoLrMJVg2BxGgu6wIZbXqZx9vWIsCzaPN2hBBCFO760YcOBJG5PZGy0XiFVW4miS1ZsgR3d3f69u1rl/7yyy/Tpk0bfHx82LlzJxMnTuTSpUvMnDkTgJiYGIKDg+1e4+/vb7vm7e1NTEyMLe3GPDExMQXWZ9q0abzzzjsl0bTyx2yCrBTr3okWM6jmmz7ekG70BI9qpbun4oa34MzvWHQuPJ02lhTFjRc61S29+wkhRCWTu+F4giPD2RnWvDInsuIqN0HkwoULGTx4MEajfe/S+PHjbZ+3aNECg8HACy+8wLRp03Bycrq5mBIzceJEu3tfuHCBJk2alNr9ygSzCXZ/AVunQ7YDPa/OPhDQHAJbQEBL6+dV6oNGe/t1OvA/a52ABX4T+fufGjzSMojaVVxvv2whhBAAeNnOz3Z8YY3Miay4ykUQ+fvvv3PixAm++eabW+Zt3749OTk5nDlzhoYNGxIQEEBsbKxdntznufMoC8pT0DxLACcnJ7sgNTk5ucjtKZeid8Ga8XD5SN5ritYaECpa6wkwGs31tPR4yIiHqG3WRy6ds3Wvxtzgslpb6wbgjvRYnvsT1owDIOHeV5n2u7X38aXO0gsphBAlqTg9kdeHsyWIrKjKRRC5YMEC2rZtS8uWLW+ZNzIyEo1Gg5+fHwAhISH8+9//xmQyoddbv5E3btxIw4YN8fb2tuXZtGkTY8eOtZWzceNGQkJCSr4x5U3aVfhtkrXHD8DZGx6aCi0GXg8YC2PKhLhjcOkQxPwFMYesq6hNaXBhr/WRK6A53PsCNH8C9M6Fl5t8Cb55GszZ0OhhZqQ/ikW9QLdGfjQO9Li9NgshhLBTnKMPZWFNxXdXg8jU1FROnTplex4VFUVkZCQ+Pj7UrFkTsPbwrVq1io8//jjP6yMiIti9ezddunTB3d2diIgIxo0bx9NPP20LEJ966ineeecdhg8fzuuvv87hw4eZNWsWn3zyia2cV155hQcffJCPP/6Y3r17s2LFCvbu3Wu3DVClY7HAga/ht8mQYV2AROtnIPQdcPUtejl6IwS1tj5sZZshPgpiDloDy0uH4OxO6+c/jYaNk6DtEGg3HLxq5C3TlGkNIFNjoGpjYrp9yref/gnAS12kF1IIIUqat4P7RKqqatviR+ZEVlx3NYjcu3cvXbp0sT3PnWM4ZMgQFi9eDMCKFStQVZVBgwbleb2TkxMrVqxgypQpZGVlERwczLhx4+zmKnp6erJhwwbCw8Np27YtVapUYdKkSbbtfQA6dOjA8uXLeeutt3jzzTepX78+q1evplmzZqXU8jIu5i/r0PX5Pdbnfk3h4ZlQ876SKV+jhSr1rI9m1/b8TI+3Bq175kNSNOz4BP6YBY16Q/sXoVZH61C3qsIv4609mEYvGLSceX/EYTKrtA/2oW0tn5KpoxBCCBuf3DmRRRzOzjRZyDZbAGwblYuK564GkZ07d0ZV1ULzjBw50i7gu1GbNm3YtWvXLe/TokULfv/990Lz9O/fn/79+9+yrAotKwW2TLMuVFHNYHCDzhOtQZyDp71kmswMWbiHs1fTeaiJP72aB3JvsA9aTQFzHl18oOMrEDIaTvxqrcOZ3+HYz9aHX1NoPxIykyByGSga6L+YeKfq/N+ezQCEd5F9IYUQojR4OTgnMvHaymydRsHVUAKLKEWZVC7mRIpSpKpw+Rgc/RH2L4GUS9b0Jo9B2DTwrFasYj/ecILdUfEAfL3rLF/vOksVNyd6NgsoPKDUaKHxw9ZH7FHYMw8OrrAu6Pn5lev5ur8HdbuweMMJMkxmmlfz5IH6VYpVVyGEEIXLXViTkpWDyWxBry18PnziDafVKKW5xZu4qySIrIxUFWIPWwPHI6vh6snr17yDoddHUD+0wJffys7TV5i/IwqA13o0JCoujQ1HY7mSmmUXUPZoZu2hbB/sm39A6d8EHvkUQidbF/bsmQeJ0dBqMNz3EimZJhbvPANYV2TLLyohhCgdHs56NApYVOuG437uhR/mkBtEesh8yApNgsjKQlXh0kFr4Hj0R4g/ff2a1gD1Qq29j036WBfDFFNypol/rTyIqsKge2vw0rWjB/+TY2Hn6Sv8cuiSLaD8365o/rcrmipuBno3D2RsaAPbCkA7zt7QYQzc9xJcPQVVGoCisGx3NMmZOdSt6kpY04K3YxJCCHF7tBoFT2c9CekmEtJMtwwibXtEShBZoUkQWdElnoM/v7IGjglnrqdrnaD+Q9agsUEYGEtmW5wpPx3hYlImNX1ceKv39c3XDToNnRv60bmhH++bLfxx6gpr/7rE+iOxXEnNZknEWX47dpnZT7WmdU3v/AvXaKFqQ8A653L+79bezlGd66EpaK6lEEKIEuHtarAGkUXY5if3tBpZVFOxlZuzs0UxRG2HLx+wrnJOOGPd4Lvxo/DEQnjtNAxcBi36l1gAufavS3y//wIaBT55siWuTvn/j6LXWgPKGU+0ZO9boSx67h5q+7pwITGDAV9GsOiPqFsuuFq17zxXUrOo5uXMY62CSqT+QgghCubIhuMVeaNxs9nM22+/TXBwMM7OztStW5d3333X7u+WqqpMmjSJwMBAnJ2dCQ0N5eTJ61PHsrKyeOaZZ/Dw8KBBgwb89ttvdvf48MMPGTNmzB1rU3FJT2RFtXchrJ0AlhwIbAX3j4X63cFQOscBXk7O5M0f/gLgpc71irzVjl6roUsjP9rW9ub1bw/x6+EY3vn5KH+eieeDfi3wMOb9BWQyW/hym3U4fmSnOrec4C2EEOL2eTmwV2TuRuMVcU7k9OnTmTt3LkuWLKFp06bs3buXoUOH4unpycsvvwzAjBkz+Oyzz1iyZAnBwcG8/fbbhIWFcfToUYxGI/PmzWPfvn1ERETw66+/8tRTTxEbG4uiKERFRfHVV1+xd+/eW9Tk7pO/vhWNOccaPK4ZZw0gm/eHYeug6eOlFkCqqsqEbw+RmG6iWTUPXu5W3+EyPIx6Ph/chsmPNEGvVVj7VwyP/ncHRy4m5cn788GLnE/IoIqbgSfvyWczciGEECXOtldkkYazK+652Tt37uSxxx6jd+/e1K5dmyeeeILu3buzZ491b2VVVfn000956623eOyxx2jRogVLly7l4sWLrF69GoBjx47x6KOP0rRpU8LDw4mLi+PKlSsAjBo1iunTp+PhUfZPX5OeyBKWk5ODyWT94dFoNGi1WsxmMxaLxZYnNz0nJ8eu+1ur1aLRaApMzy03l06ns90TgIwEtN8PR3NmOwDmzm9h6fAKoMC1Yx8tFgtms9lWhqIo6HS6AtMLqvuN6cv2nGPb33EYdBo+7NccRTVjMpmL1abnOtSmZXUvRi/fz5mr6Tz++U4m927EoPa1UBSF7GwTc7ZYTzl6LqQWRr22VNpUqu/TLdJL630qy23SYEFRc68pqIoGVAsK1+uS+3l5aVNFfJ+kTZW7TbkLH6+kZNraUFCbEq8Fmu5OWrtrZa1Nue8TQEpKCsnJybbrTk5OODk5cbMOHTowb948/v77bxo0aMDBgwfZsWMHM2fOBKyn78XExBAaen2XE09PT9q3b09ERAQDBw6kZcuWfP3112RkZLB+/XoCAwOpUqUKy5Ytw2g08vjjj+e5b1kkQWQJi4iIwMXFBYCaNWvSunVrDh06RHR0tC1Pw4YNadSoEXv27CEuLs6W3qpVK2rVqsX27dtJSUmxpYeEhODn58eGDRvsfvC6dOmCs7Mza9euxS3zIu3/+QS3rFhUvSsZPT9l4zkn+PVXwPqD27t3b65cuUJERIStDHd3d7p27cq5c+eIjIy0pVetWpUOHTpw8uRJTpw4YUu/uU2XM2DGIS2g8EaPRiScOcraP2+vTc2D3BhdP5X/ndJwNBH+/eNR/jyTwBsPBTPvp985HafFqFUJSD0JNCjxNpXm+3SjXr16kZGRwZYtW2xppfU+leU2Xb58maa6WMiIBSBd40qMsSbepqt451yx5Y/XWM9TLw9tqojvk7RJ2pR79OHhk1GsVU8X2qarKdZ9e8+cOMLa+MNltk21atWyld2kyfXFoACTJ09mypQp3OyNN94gOTmZRo0a2YLW//znPwwePBiAmJgYAPz9/e1e5+/vb7s2bNgwDh06RJMmTahSpQorV64kISGBSZMmsXXrVt566y1WrFhB3bp1WbhwIdWqFW/P5tKmqLdawSCK5Pz589SoUYOoqCjbm32n/ns1n1iP9ocRKFnJqJ41YND/gX+zUv9PL9uUw5Pz93DofDId6vryv+HtsVjMJfYfucWi8tWOM8z87SQWFer7uaEAf19OZVSnYMY/VP+u//dakXoZ7labrly5wrxtp3Bx98y9km9PZFpKMqO61MfT07PMt6kivk/SJmnTt/su8Np3h3iwQRXmP9Om0Db1mbuLIxeT+eqZ1nRuULXMtkmj0XDmzBmCg4M5evSoXbBWUE/kihUrmDBhAh9++CFNmzYlMjKSsWPHMnPmTIYMGcLOnTvp2LEjFy9eJDAw0Pa6AQMGoCgK33zzTZ4yAYYOHUqrVq0IDg7mzTffZPfu3cyYMYPDhw/z3Xff5fuau016IkuYTqdDr7efA6LVatFq8x77lPvDVNT0m8tFVWHXXHQb/g2qBWqGoAz4Gtyq5p8f6w+TRpN3KmxB6QXVXavVMm/LPxw6n4yHUcfHA1qi0ShoNLfZppvSR3drQLtgX8b83wFOXk4FwKjXMLxTXVuekmxTqbxPxUivbG2yoEFVbqqnouHG/3BVrNs4lZc2VcT3SdpUuduUO78xMSMnT51ufp47J9LX3Tnf+peVNt2Y7u7uXqR5iBMmTOCNN95g4MCBADRv3pyzZ88ybdo0hgwZQkCAdd/i2NhYuyAyNjaWVq1a5Vvmli1bOHLkCPPnz2fChAn06tULV1dXBgwYwOzZs29Zp7tFFtaUVznZ8NMYWD/RGkC2ehqe/dEWQJa2g+cS+WyzdbuCd/s0I9DTudTudV8dX9a+/AAd6voC8FyHYKq45f3vUAghROnxuTYnMrEoC2sq8BY/6enpeYJarVZr6/0MDg4mICCATZs22a4nJyeze/duQkJC8pSXmZlJeHg4X375pa0nNbdn12Qy2fXCljXSE1kepV2Bb56B6J2gaKznSN/3EtyhY/8yss2M+yYSs0Xl4RaBPNaq9OdqVHV34uvh7Ym6kkrdqm6lfj8hhBD2chfWxN9in0iT2UJKlnW4uiJuNv7II4/wn//8h5o1a9K0aVMOHDjAzJkzGTZsGGAdbh87dizvvfce9evXt23xExQURJ8+ffKU9+6779KrVy9at24NQMeOHZkwYQJDhw5l9uzZdOzY8U42zyESRJZHFjMkngUnD+vG4fUfumO3VlWV99ce458rafh7OPFen2Z37N5ajUI9P/c7dj+RP4vFQmJiYpHyenl55TsMJYQof3IX1qRk5mAyWwrcozc54/r8SA9jxQsz/vvf//L222/z0ksvcfnyZYKCgnjhhReYNGmSLc9rr71GWloaI0eOJDExkfvvv59169ZhNNofF3n48GFWrlxpt3DoiSeeYOvWrTzwwAM0bNiQ5cuX36mmOUwW1pSQ3IU1586do3r16qV/w0uHQOdkOwawtKVn5/BT5EW+3nWWIxetWyB8PfxeHqh/Z4bPRdkRHx/PzDUHMLoWHtBnpqUw/uHW+PgUvvF8fHw8n285hauHV6H50pITealLvVuWJ4QoHWaLSr1/r0VV4c9/h1LVPf9pRf/EpdL14224O+n4652wO1xLx93xv98VSMX7F6GyCGxxR27zT1wq/9sVzap950jJtA5POOk0jA1tIAFkJWZ0db9l0CeEqFi0GgUvZ73t/OyCgsjc02o8K+BG48KeBJEijxyzhU3HL/O/XWf5/eT1ffpq+brwdPtaPNG2um1ujBBCiMrD28VgDSILmRdpW1QjQWSFJ0GksIlLyWLFnmiW74nmUlImYF2r062RH0/fV4tO9aui0dyZxTtCCCHKHm9XA1xJK/Tow8QM6zXPCrgyW9iTILKCS8owse9sPAlpJhIzTCSlZ5OYYSIxPe/z5EwTuTNkfVyt51I/dW9Navi43N1GCCGEKBNyF9fEp5kKzHN9ex8ZsaroJIisoOLTslmw4x+W7DxLalbOrV9wTZuaXjwTUotezQNx0uXdrFUIIUTl5X1tiLrwnkiZE1lZSBBZwcSlZPHV7//wv11nSc+2blBa29eFmr6ueDnr8XLR4+Wsx9PFYP2Ym+aix9vFgK9s4i2EEKIAuRuOFzYnMrECbzQu7EkQWUHEJGXy5fbTLN8dTVaOddf8ZtU8GNO1Pg819pe5jEIIIW5b7ubh8YX0ROYeeShzIis+CSLLuQuJGXyx9TTf/HmObLM1eGxVw4tXutWnc8OqKHfoFBshhBAVn4/rtfOz0wuZE5khq7MrCwkiy6noq+l8vvUU3+0/j8lsXQ1zT21vXu5Wn/vrVZHgUQghRIm7vrCmsOHs3NXZsrCmopMgshz6Jy6Vhz7ZjtliDR471PVlTNf63FfHR4JHIYQQpSZ3j+DEoiyskeHsCk+CyHKoTlU32tbyxqjX8nLXerSrLcfACSGEKH1F6YmUzcYrDwkiy6klQ+/F2SBb8AghhLhzcldnJ2fmkGO2oNNq7K6rqipzIisRza2ziLJIAkghhBB3mqezntxZU7nD1jdKyzaTc22qlWw2XvFJECmEEEKIItFqFNtcx/z2isydK2nQajDqJcSo6OQdFkIIIUSR+RQyLzLphtNqZKFnxSdBpBBCCCGKzMt29GHe4ewkOa2mUpEgUgghhBBFZjv6MJ9tfhJlUU2lIkGkEEIIIYrMq5Dh7NyTbGSPyMrhrgaR27dv55FHHiEoKAhFUVi9erXd9eeeew5FUewePXr0sMsTHx/P4MGD8fDwwMvLi+HDh5OammqX59ChQzzwwAMYjUZq1KjBjBkz8tRl1apVNGrUCKPRSPPmzVm7dm2Jt1cIIYQo73wK2XD8+rnZsjK7MrirQWRaWhotW7Zkzpw5Bebp0aMHly5dsj3+7//+z+764MGDOXLkCBs3bmTNmjVs376dkSNH2q4nJyfTvXt3atWqxb59+/jwww+ZMmUK8+bNs+XZuXMngwYNYvjw4Rw4cIA+ffrQp08fDh8+XPKNFkIIIcqx6xuO550TmZhhDSxlOLtyuKubjffs2ZOePXsWmsfJyYmAgIB8rx07dox169bx559/0q5dOwD++9//0qtXLz766COCgoJYtmwZ2dnZLFy4EIPBQNOmTYmMjGTmzJm2YHPWrFn06NGDCRMmAPDuu++yceNGZs+ezRdffFGCLRZCCCHKN+9rAWK+PZGysKZSKfNzIrdu3Yqfnx8NGzZk1KhRXL161XYtIiICLy8vWwAJEBoaikajYffu3bY8nTp1wmC43rUeFhbGiRMnSEhIsOUJDQ21u29YWBgREREF1isrK4vk5GTbIyUlpUTaK4QQQpRluednx+e3sCb9+hY/ouIr00Fkjx49WLp0KZs2bWL69Ols27aNnj17YjabAYiJicHPz8/uNTqdDh8fH2JiYmx5/P397fLkPr9Vntzr+Zk2bRqenp62R5MmTW6vsUIIIUQ5YFudXdg+kdITWSmU6bOzBw4caPu8efPmtGjRgrp167J161a6det2F2sGEydOZPz48bbnFy5ckEBSCCFEheddyD6R17f4kYU1lUGZ7om8WZ06dahSpQqnTp0CICAggMuXL9vlycnJIT4+3jaPMiAggNjYWLs8uc9vlaeguZhgnavp4eFhe7i7u99e44QQQohyIHdhTVKGiRyzxe5a0rUhbpkTWTmUqyDy/PnzXL16lcDAQABCQkJITExk3759tjybN2/GYrHQvn17W57t27djMl3/j2njxo00bNgQb29vW55NmzbZ3Wvjxo2EhISUdpOEEEKIcsXTWU/uiYa5PY+5EmU4u1K5q0FkamoqkZGRREZGAhAVFUVkZCTR0dGkpqYyYcIEdu3axZkzZ9i0aROPPfYY9erVIywsDIDGjRvTo0cPRowYwZ49e/jjjz8YPXo0AwcOJCgoCICnnnoKg8HA8OHDOXLkCN988w2zZs2yG4p+5ZVXWLduHR9//DHHjx9nypQp7N27l9GjR9/xr4kQQghRlum0GjyMeVdoZ+dYSM+2rlmQLX4qh7saRO7du5fWrVvTunVrAMaPH0/r1q2ZNGkSWq2WQ4cO8eijj9KgQQOGDx9O27Zt+f3333FycrKVsWzZMho1akS3bt3o1asX999/v90ekJ6enmzYsIGoqCjatm3Lq6++yqRJk+z2kuzQoQPLly9n3rx5tGzZkm+//ZbVq1fTrFmzO/fFEEIIIcqJ3MU1N+4VmbuoRlHA3ShBZGVw2wtrkpOT2bx5Mw0bNqRx48YOvbZz586oqlrg9fXr19+yDB8fH5YvX15onhYtWvD7778Xmqd///7079//lvcTQgghKjsv2+Ka6z2RSdc2Gvcw6tFqlLtSL3FnOdwTOWDAAGbPng1ARkYG7dq1Y8CAAbRo0YLvvvuuxCsohBBCiLLFxyXvNj9ybnbl43AQuX37dh544AEAfvjhB1RVJTExkc8++4z33nuvxCsohBBCiLIlvw3Hk2zb+0gQWVk4HEQmJSXh4+MDwLp16+jXrx8uLi707t2bkydPlngFhRBCCFG2XD/68PqcSOmJrHwcDiJr1KhBREQEaWlprFu3ju7duwOQkJCA0Wgs8QoKIYQQomyx9UTeOJwtG41XOg4vrBk7diyDBw/Gzc2NWrVq0blzZ8A6zN28efOSrp8QQgghypj85kTmbjTu6VymD8MTJcjhd/qll17i3nvv5dy5czz00ENoNNbOzDp16sicSCGEEKISyO1tTMhvTqSz9ERWFsX6d6Fdu3a0a9fOLq13794lUiEhhBBClG25+0TeeH52oiysqXQcDiLNZjOLFy9m06ZNXL58GYvF/tzMzZs3l1jlhBBCCFH2+LhaA8V42eKnUnM4iHzllVdYvHgxvXv3plmzZiiKbCgqhBBCVCa5w9nJmSZyzBZ0Wo2cm10JORxErlixgpUrV9KrV6/SqI8QQgghyjiva4GiqlrnQvq6OZEsq7MrHYe3+DEYDNSrV6806iKEEEKIckCn1eBhtPZD5c6LTLy2yEbmRFYeDgeRr776KrNmzSr0zGshhBBCVGzXF9dkY7GoN6zOliCysnB4OHvHjh1s2bKFX3/9laZNm6LX23+zfP/99yVWOSGEEEKUTd6uBs5cTSc+LZuUrBws1/qWPCSIrDQcDiK9vLx4/PHHS6MuQgghhCgnvK/NfUxMz7bNhzTqNRj12rtZLXEHORxELlq0qDTqIYQQQohyJDeIjE8z2bb3kY3GK5din00UFxfHiRMnAGjYsCFVq1YtsUoJIYQQomzL3SsyIT2bxAxZVFMZObywJi0tjWHDhhEYGEinTp3o1KkTQUFBDB8+nPT09NKooxBCCCHKGK8bzs/O7YmU+ZCVi8NB5Pjx49m2bRs///wziYmJJCYm8uOPP7Jt2zZeffXV0qijEEIIIcqYG1dny8rsysnh4ezvvvuOb7/9ls6dO9vSevXqhbOzMwMGDGDu3LklWT8hhHCYxWIhMTGxSPkANJpb/z/t5eVVpHxCVBa5cyIT0k3Xg0gZzq5UHA4i09PT8ff3z5Pu5+cnw9lCiDIhMTGRmWsOYHR1LzRfQuwFFJ0BL9/C53RnpqUw/uHW+Pj4lGQ1hSjXvK8FjNbhbOucSDnysHJxOIgMCQlh8uTJLF26FKPRCEBGRgbvvPMOISEhJV5BIYQoDqOrO64eXoXmSU9JQqN3umU+IUReucPZ8enX50TKkYeVi8NB5KxZswgLC6N69eq0bNkSgIMHD2I0Glm/fn2JV1AIIYQQZU9uwJiUYSJBeiIrJYeDyGbNmnHy5EmWLVvG8ePHARg0aBCDBw/G2dm5xCsohBBCiLInd/6jqsLZq+l2aaJyKNY+kS4uLowYMaKk6yKEEEKIckKv1eBh1JGcmWMLIqUnsnIpUhD5008/0bNnT/R6PT/99FOheR999NESqZgQQgghyjZvVwPJmTlkm607HciJNZVLkYLIPn36EBMTg5+fH3369Ckwn6IomM3mkqqbEEIIIcowbxeDrRcSZDi7silSEJm7l9rNnwshhBCi8spdoZ3LU4LISsXhnXOXLl1KVlZWnvTs7GyWLl1aIpUSQgghRNl3Y8+jRgE3Q7GWWohyyuEgcujQoSQlJeVJT0lJYejQoSVSKSGEEEKUfT437Avp6axHo1HuYm3EneZwEKmqKoqS95vk/PnzeHp6lkilhBBCCFH2ed8wnC0bjVc+Re53bt26NYqioCgK3bp1Q6e7/lKz2UxUVBQ9evQolUoKIYQQouzxvqknUlQuRQ4ic1dlR0ZGEhYWhpubm+2awWCgdu3a9OvXr8QrKIQQQoiyycf1euAoQWTlU+QgcvLkyQDUrl2bgQMH4uTkVGqVEkIIIUTZd+MQtmzvU/k4PCeySZMmREZG5knfvXs3e/fudais7du388gjjxAUFISiKKxevdp2zWQy8frrr9O8eXNcXV0JCgri2Wef5eLFi3Zl1K5d2zbMnvv44IMP7PIcOnSIBx54AKPRSI0aNZgxY0aeuqxatYpGjRphNBpp3rw5a9eudagtQgghRGVz4xY/XtITWek4HESGh4dz7ty5POkXLlwgPDzcobLS0tJo2bIlc+bMyXMtPT2d/fv38/bbb7N//36+//57Tpw4ke+JOFOnTuXSpUu2x5gxY2zXkpOT6d69O7Vq1WLfvn18+OGHTJkyhXnz5tny7Ny5k0GDBjF8+HAOHDhAnz596NOnD4cPH3aoPUIIIURlYjcnshItrLlw4QJPP/00vr6+ODs707x5c7uONFVVmTRpEoGBgTg7OxMaGsrJkydt17OysnjmmWfw8PCgQYMG/Pbbb3blf/jhh3axTFnl8IZOR48epU2bNnnSW7duzdGjRx0qq2fPnvTs2TPfa56enmzcuNEubfbs2dx7771ER0dTs2ZNW7q7uzsBAQH5lrNs2TKys7NZuHAhBoOBpk2bEhkZycyZMxk5ciQAs2bNokePHkyYMAGAd999l40bNzJ79my++OILh9okhBBCVBY3DmFXljmRCQkJdOzYkS5duvDrr79StWpVTp48ibe3ty3PjBkz+Oyzz1iyZAnBwcG8/fbbhIWFcfToUYxGI/PmzWPfvn1ERETw66+/8tRTTxEbG4uiKERFRfHVV185PLp7NzjcE+nk5ERsbGye9EuXLtmt2C4NSUlJKIqCl5eXXfoHH3yAr68vrVu35sMPPyQnJ8d2LSIigk6dOmEwXP8PKSwsjBMnTpCQkGDLExoaaldmWFgYERERpdcYIYQQopzTazW4G61/+yvLcPb06dOpUaMGixYt4t577yU4OJju3btTt25dwNoL+emnn/LWW2/x2GOP0aJFC5YuXcrFixdt0/aOHTvGo48+StOmTQkPDycuLo4rV64AMGrUKKZPn46Hh8fdamKRORz1de/enYkTJ/Ljjz/a9oVMTEzkzTff5KGHHirxCubKzMzk9ddfZ9CgQXZf2Jdffpk2bdrg4+PDzp07mThxIpcuXWLmzJkAxMTEEBwcbFeWv7+/7Zq3tzcxMTG2tBvzxMTEFFifrKwsu5N7UlJSAMjJycFkMgGg0WjQarWYzWa74yJz03NyclBV1Zau1WrRaDQFpueWmys3aL8xaC4sXa/XY7FY7M43VxQFnU5XYHpBdZc2Vd42WeurgqqiYH8Mqnrt/1IFCxostp+HW7VJgwVFzb2moCoaUC0oXK9L7udFaVNOTo4tv6JarPW9sY6KgqKa0aKioKKoZru6522TavezDWX/fYKK970nbSqbbfJxNZCSmYOb0/W2lbc2gfXveHJysu26k5NTvouIf/rpJ8LCwujfvz/btm2jWrVqvPTSS4wYMQKAqKgoYmJi7DqnPD09ad++PREREQwcOJCWLVvy9ddfk5GRwfr16wkMDKRKlSosW7YMo9HI448/nue+ZZHDQeRHH31Ep06dqFWrFq1btwas2/74+/vz9ddfl3gFwbrIZsCAAaiqyty5c+2ujR8/3vZ5ixYtMBgMvPDCC0ybNq1UV5BPmzaNd955J096REQELi4uANSsWZPWrVtz6NAhoqOjbXkaNmxIo0aN2LNnD3Fxcbb0Vq1aUatWLbZv324LSgFCQkLw8/Njw4YNdj94Xbp0wdnZOc8ioF69epGRkcGWLVtsaTqdjt69e3PlyhW7HlZ3d3e6du3KuXPn7BZMVa1alQ4dOnDy5ElOnDhhS5c2SZsAnKiCgoXgjL/t2hTl3ACdmkONzH9ABxERsbds0+XLl2mqi4UM6whHusaVGGNNvE1X8c65Yssfr3EGKHKbvBRPsvGmWmYUBjXbln7JqQYZWjdqZZwi2NsCZEBGIueMdchRdPm2yYkcu7qXl/epIn7vSZvKXpt6NQ9kZcRprhz/k7Wny1+bcstu0qSJXV0nT57MlClTuNk///zD3LlzGT9+PG+++SZ//vknL7/8MgaDgSFDhtg6oArrnBo2bBiHDh2iSZMmVKlShZUrV5KQkMCkSZPYunUrb731FitWrKBu3bosXLiQatWq5alHWaCoN4blRZSWlsayZcs4ePAgzs7OtGjRgkGDBqHXF78rW1EUfvjhB9t+lLlyA8h//vmHzZs34+vrW2g5R44coVmzZhw/fpyGDRvy7LPPkpycbLfye8uWLXTt2pX4+Hi8vb2pWbMm48ePZ+zYsbY8kydPZvXq1Rw8eDDf+9zcE3nhwgWaNGlCVFSU7c2W/16lTRWxTQkJCcz7PQpXd69CeyLTU5J4/oE6eHt7F9qmK1euMG/bKVzcc0+8yr8nMi0lmVFd6uPp6XnLNiUkJPDV71G4eHgX2hN59eI5FL0TPlX9Cu2JTEtJZOQDwXZznsr6+wQV73tP2lR225SdnW13ml15atOZM2cIDg7m6NGjdsFaQT2RBoOBdu3asXPnTlvayy+/zJ9//klERAQ7d+6kY8eOXLx4kcDAQFueAQMGoCgK33zzTZ4ywXqsdKtWrQgODubNN99k9+7dzJgxg8OHD/Pdd9/l+5q7rViTGF1dXW2LUkpTbgB58uRJtmzZcssAEqy9ohqNBj8/P8D6X9K///1v25AawMaNG2nYsKHtD0JISAibNm2yCyI3btxISEhIgfe5+Zsrtwtcp9PlCaa1Wi1arTZPGQXNIS0ovaAg3ZF0jUaDRpN3KmxB6QXVXdpUedtkzaOAoqCSt2wAFS0WNHY/D4W1yYIGVbmpLEXDjf/hqihFbpNOp7PlV5X8p36rihYzChoUu3vn3yYl35/tsvw+FTdd2iRtAsfbdOO6gxuVpza5u7sXaR5iYGBgnl7Lxo0b2wK93IW+sbGxdkFkbGwsrVq1yrfMLVu2cOTIEebPn8+ECRPo1asXrq6uDBgwgNmzZ9+yTndLkYLIn376iZ49e6LX6/npp58KzZvfFjwFSU1N5dSpU7bnUVFRREZG4uPjQ2BgIE888QT79+9nzZo1mM1mWzewj48PBoOBiIgIdu/eTZcuXXB3dyciIoJx48bx9NNP2wLEp556infeeYfhw4fz+uuvc/jwYWbNmsUnn3xiu+8rr7zCgw8+yMcff0zv3r1ZsWIFe/futdsGSAghhBCiY8eOdsPoAH///Te1atUCIDg4mICAADZt2mQLGpOTk9m9ezejRo3KU15mZibh4eEsW7bM1pOa22NqMpnsemHLmiIFkX369CEmJgY/P788w803UhTFocbu3buXLl262J7nzm8cMmQIU6ZMsQWsN0fuW7ZsoXPnzjg5ObFixQqmTJlCVlYWwcHBjBs3zm6epKenJxs2bCA8PJy2bdtSpUoVJk2aZNeT2qFDB5YvX85bb73Fm2++Sf369Vm9ejXNmjUrcluEEEIIUfGNGzeODh068P777zNgwAD27NnDvHnzbB1PiqIwduxY3nvvPerXr2/b4icoKCjfGOrdd9+lV69etnUmHTt2ZMKECQwdOpTZs2fTsWPHO9k8hxQpiLxxbsGNn9+uzp07U9iUzFtN12zTpg27du265X1atGjB77//Xmie/v37079//1uWJYQQQojK65577uGHH35g4sSJTJ06leDgYD799FMGDx5sy/Paa6+RlpbGyJEjSUxM5P7772fdunUYjUa7sg4fPszKlSvtFg498cQTbN26lQceeICGDRuyfPnyO9U0h5Xuxo5CCCGEEBXMww8/zMMPP1zgdUVRmDp1KlOnTi20nGbNmtmdZAPWOaCff/45n3/+eYnUtTQVKYj87LPPilzgyy+/XOzKCCGEEEKI8qFIQeSNi1AA4uLiSE9Pt50ck5iYiIuLC35+fhJECiGEEEKUQWazmcWLF7Np0yYuX76cZ4ri5s2bHSqvSEFkVFSU7fPly5fz+eefs2DBAho2bAjAiRMnGDFiBC+88IJDNxdCCCGEEHfGK6+8wuLFi+nduzfNmjWz29uzOByeE/n222/z7bff2gJIsO4G/8knn/DEE0/YTSwVQgghhBBlw4oVK1i5ciW9evUqkfLy34W3EJcuXcqz0zxYu0hjY2NLpFJCCCGEEKJkGQwG6tWrV2LlORxEduvWjRdeeIH9+/fb0vbt28eoUaPsDhsXQgghhBBlx6uvvsqsWbNuuYViUTk8nL1w4UKGDBlCu3btbMcW5eTkEBYWxvz580ukUkIIIYQQomTt2LGDLVu28Ouvv9K0adM8x09+//33DpXncBBZtWpV1q5dy99//83x48cBaNSoEQ0aNHC0KCGEEEIIcYd4eXnx+OOPl1h5xd5svHbt2qiqSt26dQs81FwIIYQQQpQNixYtKtHyHJ4TmZ6ezvDhw3FxcaFp06ZER0cDMGbMGD744IMSrZwQQgghhChZcXFx7Nixgx07dhAXF1fschwOIidOnMjBgwfZunWr3RmQoaGhfPPNN8WuiBBCCCGEKD1paWkMGzaMwMBAOnXqRKdOnQgKCmL48OGkp6c7XJ7DQeTq1auZPXs2999/v90mlU2bNuX06dMOV0AIIYQQQpS+8ePHs23bNn7++WcSExNJTEzkxx9/ZNu2bbz66qsOl+fwZMa4uDj8/PzypKelpd32zudCCCGEEKJ0fPfdd3z77bd07tzZltarVy+cnZ0ZMGAAc+fOdag8h4PIdu3a8csvvzBmzBgAW+A4f/58QkJCHC1OCFFBWSwWEhISbpkvISEBSmjPMiGEEAVLT0/H398/T7qfn1+xhrMdDiLff/99evbsydGjR8nJyWHWrFkcPXqUnTt3sm3bNocrIISomDLTUpi76SpevlULzZcQewFnT19c71C9hBCisgoJCWHy5MksXbrUtq4lIyODd955p1gdgQ4Hkffffz8HDx5k2rRpNG/enA0bNtCmTRsiIiJo3ry5wxUQQlRcRld3XD28Cs2TnpJ0ZyojhBCV3KxZswgLC6N69eq0bNkSgIMHD2I0Glm/fr3D5TkURJpMJl544QXefvttvvrqK4dvJoQQQggh7o5mzZpx8uRJli1bZjswZtCgQQwePBhnZ2eHy3MoiNTr9Xz33Xe8/fbbDt9ICCGEEELcXS4uLowYMaJEynJ4OLtPnz6sXr2acePGlUgFhBBCCCFE6fjpp5/o2bMner2en376qdC8jz76qENlOxxE1q9fn6lTp/LHH3/Qtm1bXF3tp8O//PLLjhYphBBCCCFKQZ8+fYiJicHPz48+ffoUmE9RFMxms0NlOxxELliwAC8vL/bt28e+ffvyVECCSCGEEEKIssFiseT7eUlwOIiMiooq0QoIkR+LxUJiYmKR8np5eaHROHz4khBCCFGpLF26lCeffBInJye79OzsbFasWMGzzz7rUHkOB5E3Uq9tECwn1YiSlpiYyMw1BzC6uheaLzMthfEPt8bHx+cO1UwIIYQon4YOHUqPHj3ynDyYkpLC0KFDHQ4ii9V9s2DBApo1a4bRaMRoNNKsWTPmz59fnKKEKFDuHoOFPW4VZAohhBDCSlXVfDv+zp8/j6enp8PlOdwTOWnSJGbOnMmYMWNsu5tHREQwbtw4oqOjmTp1qsOVEEIIIYQQpaN169YoioKiKHTr1g2d7nr4ZzabiYqKokePHg6X63AQOXfuXL766isGDRpkS3v00Udp0aIFY8aMkSBSCCGEEKIMyV2VHRkZSVhYGG5ubrZrBoOB2rVr069fP4fLdTiINJlMtGvXLk9627ZtycnJcbgCQgghhBCi9EyePBmA2rVr8+STT9rOzb5dDs+JfOaZZ5g7d26e9Hnz5jF48OASqZQQQgghhChZQ4YMwWg0kp2dzfnz54mOjrZ7OKpYq7MXLFjAhg0buO+++wDYvXs30dHRPPvss4wfP96Wb+bMmcUpXgghhBBClLCTJ08ybNgwdu7caZeeu+Cm1DcbP3z4MG3atAHg9OnTAFSpUoUqVapw+PBhWz7Z9kcIIYQQoux47rnn0Ol0rFmzhsDAwNuO1RwOIrds2XJbNxRCCCGEEHdeZGQk+/bto1GjRiVSnhzzIYQQQghRCTRp0oQrV66UWHkSRAohhBBCVALTp0/ntddeY+vWrVy9epXk5GS7h6PuahC5fft2HnnkEYKCglAUhdWrV9tdV1WVSZMmERgYiLOzM6GhoZw8edIuT3x8PIMHD8bDwwMvLy+GDx9OamqqXZ5Dhw7xwAMPYDQaqVGjBjNmzMhTl1WrVtGoUSOMRiPNmzdn7dq1Jd5eIYQQQoi7JTQ0lF27dtGtWzf8/Pzw9vbG29sbLy8vvL29HS7vts7Ovl1paWm0bNmSYcOG0bdv3zzXZ8yYwWeffcaSJUsIDg7m7bffJiwsjKNHj9r2OBo8eDCXLl1i48aNmEwmhg4dysiRI1m+fDkAycnJdO/endDQUL744gv++usvhg0bhpeXFyNHjgRg586dDBo0iGnTpvHwww+zfPly+vTpw/79+2nWrNmd+4IIIYQQQpSSkl7XUqQgsk2bNmzatAlvb2+mTp3Kv/71L1xcXG775j179qRnz575XlNVlU8//ZS33nqLxx57DIClS5fi7+/P6tWrGThwIMeOHWPdunX8+eeftg3Q//vf/9KrVy8++ugjgoKCWLZsGdnZ2SxcuBCDwUDTpk2JjIxk5syZtiBy1qxZ9OjRgwkTJgDw7rvvsnHjRmbPns0XX3xx2+0UQgghhLjbHnzwwRItr0jD2ceOHSMtLQ2Ad955J89wcWmIiooiJiaG0NBQW5qnpyft27cnIiICsJ7Z7eXlZXeCTmhoKBqNht27d9vydOrUCYPBYMsTFhbGiRMnSEhIsOW58T65eXLvk5+srCy7eQQpKSm332ghhBBCiFL0+++/8/TTT9OhQwcuXLgAwNdff82OHTscLqtIPZGtWrVi6NCh3H///aiqykcffWR37uKNJk2a5HAl8hMTEwOAv7+/Xbq/v7/tWkxMDH5+fnbXdTodPj4+dnmCg4PzlJF7zdvbm5iYmELvk59p06bxzjvvFKNlQgghhBB33nfffcczzzzD4MGD2b9/P1lZWQAkJSXx/vvvO7wepEg9kYsXL8bX15c1a9agKAq//vorP/zwQ57HzQtjKrKJEyeSlJRkexw9evRuV0kIIYQQokDvvfceX3zxBV999RV6vd6W3rFjR/bv3+9weUXqiWzYsCErVqwAQKPRsGnTpjw9gCUtICAAgNjYWAIDA23psbGxtGrVypbn8uXLdq/LyckhPj7e9vqAgABiY2Pt8uQ+v1We3Ov5cXJywsnJyfa8OEvjhRBCCCHulBMnTtCpU6c86Z6eniQmJjpcnsNb/FgsllIPIAGCg4MJCAhg06ZNtrTk5GR2795NSEgIACEhISQmJrJv3z5bns2bN2OxWGjfvr0tz/bt2zGZTLY8GzdupGHDhrbl7CEhIXb3yc2Tex8hhBBCiPIuICCAU6dO5UnfsWMHderUcbi8Yu0Tefr0acaMGUNoaCihoaG8/PLLtnO0HZGamkpkZCSRkZGAdTFNZGQk0dHRKIrC2LFjee+99/jpp5/466+/ePbZZwkKCqJPnz4ANG7cmB49ejBixAj27NnDH3/8wejRoxk4cCBBQUEAPPXUUxgMBoYPH86RI0f45ptvmDVrFuPHj7fV45VXXmHdunV8/PHHHD9+nClTprB3715Gjx5dnC+PEEIIIUSZM2LECF555RV2796NoihcvHiRZcuW8a9//YtRo0Y5XJ7D+0SuX7+eRx99lFatWtGxY0cA/vjjD5o2bcrPP//MQw89VOSy9u7dS5cuXWzPcwO7IUOGsHjxYl577TXS0tIYOXIkiYmJ3H///axbt862RyTAsmXLGD16NN26dUOj0dCvXz8+++wz23VPT082bNhAeHg4bdu2pUqVKkyaNMm2vQ9Ahw4dWL58OW+99RZvvvkm9evXZ/Xq1bJHpBBCCCEqjDfeeAOLxUK3bt1IT0+nU6dOODk58a9//YsxY8Y4XJ6iqqrqyAtat25NWFgYH3zwQZ6KbdiwoVgTMyuC8+fPU6NGDc6dO0f16tXvdnXKvfj4eD7fcgpXD69C86UlJ/JSl3r4+PjcmYqJIr83cRfOotE74etX8NxiR/KlJMYzuE3VIp2qkJCQwP8duIKrZ+F5i3pv+T4TouKqjH+/s7OzOXXqFKmpqTRp0qTAHXduxeGeyGPHjrFy5co86cOGDePTTz8tViWEEOJWMtNSmLvpKl6+VW+ZNyH2As6evrjegXoJIUR5MWzYMGbNmoW7uztNmjSxpaelpTFmzBgWLlzoUHkOz4msWrWqbQ7jjSIjI+/IghshROVldHXH1cPrlg8nl+L9Vy2EEBXZkiVLyMjIyJOekZHB0qVLHS7P4Z7IESNGMHLkSP755x86dOgAWOdETp8+3W6xihBCCCGEuPuSk5NRVRVVVUlJSbFbW2I2m1m7dm2xOgIdDiLffvtt3N3d+fjjj5k4cSIAQUFBTJkyhZdfftnhCgghhBBCiNLj5eWFoigoikKDBg3yXFcUpVin8DkcRCqKwrhx4xg3bpztvGh3d3eHbyyEEEIIIUrfli1bUFWVrl278t1339ktEjQYDNSqVcu2NaIjHA4ibyTBoxBCCCFE2fbggw8C1v24a9asiaIoJVJusTYbF0IIIYQQ5UutWrXYsWMHTz/9NB06dODChQsAfP311+zYscPh8iSIFEIIIYSoBL777jvCwsJwdnZm//79ZGVlAZCUlMT777/vcHkSRAohhBBCVALvvfceX3zxBV999RV6vd6W3rFjx2IdFuNQEGkymejWrRsnT550+EZCCCGEEOLuOXHiBJ06dcqT7unpSWJiosPlORRE6vV6Dh065PBNhBBCCCHE3RUQEMCpU6fypO/YsYM6deo4XJ7Dw9lPP/00CxYscPhGQgghhBDi7hkxYgSvvPIKu3fvRlEULl68yLJly3j11VcZNWqUw+U5vMVPTk4OCxcu5LfffqNt27a4utqfTjtz5kyHKyGEKB0Wi6XIQxReXl5oNDJNWgghKqo33ngDi8VCt27dSE9Pp1OnTjg5OTFhwgSef/55h8tzOIg8fPgwbdq0AeDvv/+2u1ZS+w4JIUpGYmIiM9ccwOha+J6umWkpjH+4td0GtEIIISoWRVH497//zYQJEzh16hSpqak0adKEL7/8kuDgYGJiYhwqz+EgcsuWLY6+RAhxFxld3XH18Lrb1RBCCHGXZGVlMWXKFDZu3GjreezTpw+LFi3i8ccfR6vVMm7cOIfLLfaJNadOneL06dN06tQJZ2dnVFWVnkghhBBCiDJm0qRJfPnll4SGhrJz50769+/P0KFD2bVrFx9//DH9+/dHq9U6XK7DQeTVq1cZMGAAW7ZsQVEUTp48SZ06dRg+fDje3t58/PHHDldCCCGEEEKUjlWrVrF06VIeffRRDh8+TIsWLcjJyeHgwYO31QHo8Cz6cePGodfriY6OxsXFxZb+5JNPsm7dumJXRAghhBBClLzz58/Ttm1bAJo1a4aTkxPjxo277RFkh3siN2zYwPr166levbpdev369Tl79uxtVUYIIYQQQpQss9mMwWCwPdfpdLi5ud12uQ73RKalpdn1QOaKj4/HycnptiskhBBCCFFefPDBByiKwtixY21pmZmZhIeH4+vri5ubG/369SM2NtZ2PT4+nkceeQQ3Nzdat27NgQMH7MoMDw8v0emBqqry3HPP0bdvX/r27UtmZiYvvvii7Xnuw1EOB5EPPPAAS5cutT1XFAWLxcKMGTPo0qWLwxUQQgghhCiP/vzzT7788ktatGhhlz5u3Dh+/vlnVq1axbZt27h48aJdkPaf//yHlJQU9u/fT+fOnRkxYoTt2q5du9i9e7ddUHq7hgwZgp+fH56ennh6evL0008TFBRke577cJTDw9kzZsygW7du7N27l+zsbF577TWOHDlCfHw8f/zxh8MVEEKIss5isZCQkFCkvLJpuxCVQ2pqKoMHD+arr77ivffes6UnJSWxYMECli9fTteuXQFYtGgRjRs3ZteuXdx3330cO3aMgQMH0qBBA0aOHMm8efMAMJlMvPjii8yfP79Yq6ULsmjRohIr60YOB5HNmjXj77//Zvbs2bi7u5Oamkrfvn0JDw8nMDCwNOpYruTk5GAymQDQaDRotVrMZjMWi8WWJzc9JycHVVVt6VqtFo1GU2B6brm5dDqd7Z5FSdfr9VgsFsxmsy1NURR0Ol2B6QXV/U60SYMFRbXWSb3Waa5g4WaqqtqVU5bbVJT0kn6f7L+OCigaFNUCXK+7cu3zorTJWl8VVDXP+3Hj+6RFRUFFUc2oijaf/AqqokFBRXMt343pqBZbvW6s4811z69NWlTb53nza0BRUFSzfR0L+B5T0ZCZlsy8zVfw9KliS7eguXb1etmZaamMfbgNXl5e8r0nbZI2laM2AaSkpJCcnGy77uTkVOg0vfDwcHr37k1oaKhdELlv3z5MJhOhoaG2tEaNGlGzZk0iIiK47777aNmyJZs3b+b5559n/fr1tp7MGTNm0LlzZ9q1a1fgfcuSYu0T6enpyb///e+SrkuFEBERYZszWrNmTVq3bs2hQ4eIjo625WnYsCGNGjViz549xMXF2dJbtWpFrVq12L59OykpKbb0kJAQ/Pz82LBhg90PXpcuXXB2dmbt2rV2dejVqxcZGRl2G8PrdDp69+7NlStXiIiIsKW7u7vTtWtXzp07R2RkpC29atWqdOjQgZMnT3LixAlb+p1qU0REBE11QIZ1DkmUcwN0ag41Mv+x5bWg4Qj+JCYmsmPHjjLfprvxPp0+fZqmuljb1zFBV4UEQ1X8s87jYkmz5T+vWIcxitomJ6qgYCE4w/7Uqhvfp2BvgAwsGcmccWmIsyWNwKxztrzZioHzznWpYjBTxzURMhIBSNe4EmOsibfpKt45V2z5XV20nDUZ8c2OwcOcZEvPr03B3hCVaQ07q2VGYVCzbfkvOdUgQ+tGrYxTBHtbgAzISOScsQ45ii7fNjlrVFr4mQHr19GCxtomc6pdmzI9rL9S5XtP2iRtKj9tyi27SZMmdnWdPHkyU6ZMIT8rVqxg//79/Pnnn3muxcTEYDAY8PLyskv39/e3nQjzxhtvMGrUKOrWrUvt2rVZsGABJ0+eZMmSJURERPDiiy+yYcMG2rVrx1dffVWsoeY7QVFvDMuLKCEhgQULFnDs2DHA+oUfOnRopT4y7fz589SoUYOoqCiqVasGyH96t9OmuLg45v/+Dy7u1h+cgnqJUlNSGNW5Lh4eHmW+TXfjfYqLi+Or7adv+Drm3xOZlpLMqC718fDwuGWbEhISmPd7FK7uXoX2RF69eA5F74RPVb9CeyKvXDiD9lq+G9Nv7omMu3gORW+kSlU/btUTefXiOdA74eMXWGhPpF0dC+mJjLt4Fp1dHcm3TekpSbzYpYH0REqbpE3lqE1nzpwhODiYo0eP2v5+Q8E9kefOnaNdu3Zs3LjR1oPYuXNnWrVqxaeffsry5csZOnQoWVlZdq+799576dKlC9OnT89TJkDXrl155ZVXOHv2LGvWrOGXX35hxIgR+Pr6ltk9uB3uidy+fTuPPPIInp6etu7Wzz77jKlTp/Lzzz/TqVOnEq9keaLT6dDr9XZpWq0237kNuT9MRU2/udzipGs0mnznaxWUXlDdS7tNOp0OCxrrH+obqOS9p6IoDrX1brXpbr1P+X4dFfsyVKx7hRWlTdY8CihKvu+HtTwtZhQ0KNfvXUB+axim5Kkjyo0DxdZ8Sj51z69Nufe+Od0+fz51JP/vMQqso32bLNcCUfnekzYVli5tKpttcnd3t+uQKMi+ffu4fPkybdq0saWZzWa2b9/O7NmzWb9+PdnZ2SQmJtr1RsbGxhIQEJBvmYsWLcLLy4vHHnuMvn370qdPH/R6Pf3792fSpEm3rNPd4nAQGR4ezpNPPsncuXNtb5LZbOall14iPDycv/76q8QrKYSwZ7FYSExMvGW+hIQEcHywQQghRAG6deuWJ9YZOnQojRo14vXXX6dGjRro9Xo2bdpEv379ADhx4gTR0dGEhITkKS8uLo6pU6fapmWZzWZbz67JZLLrhS1rHA4iT506xbfffmsX5Wu1WsaPH2+39Y8QovQkJiYyc80BjK7uheZLiL2As6cvrrcoz5HVxxKYCiEqM3d3d5o1a2aX5urqiq+vry19+PDhjB8/Hh8fHzw8PBgzZgwhISHcd999ecobO3Ysr776qm0ovWPHjnz99dd0796defPm0bFjx9JvVDE5HES2adOGY8eO0bBhQ7v0Y8eO0bJlyxKrmBCicEZXd1w9vArNk56SVOj1XJlpKczddBUv36q3zFvUwFQIISqrTz75BI1GQ79+/cjKyiIsLIzPP/88T77169dz6tQpvv76a1va6NGj2bt3L+3bt+fee+9l8uTJd7LqDilSEHno0CHb5y+//DKvvPIKp06dskXUu3btYs6cOXzwwQelU0shRKkrSlAKRQ9MhRCisti6davdc6PRyJw5c5gzZ06hrwsLCyMsLMwuzcXFhZUrV5Z0FUtFkYLIVq1aoSiK3aqm1157LU++p556iieffLLkaieEEEIIIcqkIgWRUVFRpV0PIYQQQghRjhQpiKxVq1Zp10MIIco9RxYogRyRKIQo34p1Ys3FixfZsWMHly9fttvQE6xzJktS7dq1OXv2bJ70l156iTlz5tC5c2e2bdtmd+2FF17giy++sD2Pjo5m1KhRbNmyBTc3N4YMGcK0adPs9ozaunUr48eP58iRI9SoUYO33nqL5557rkTbIoSo2BxZoJSZlsL4h1tX6kMahBDlm8NB5OLFi3nhhRcwGAz4+vqiKIrtmqIoJR5E/vnnn3Z7JB0+fJiHHnqI/v3729JGjBjB1KlTbc9zjx0E635LvXv3JiAggJ07d3Lp0iWeffZZ9Ho977//PmAdru/duzcvvvgiy5YtY9OmTTz//PMEBgbmmfAqhBCFKeoCJSGEKO8cDiLffvttJk2axMSJE+/IMEzVqvb/0X/wwQfUrVuXBx980Jbm4uJS4C7wGzZs4OjRo/z222/4+/vTqlUr3n33XV5//XWmTJmCwWDgiy++IDg42HasUOPGjdmxYweffPKJBJFCCCGEEPlwOApMT09n4MCBd2UeT3Z2Nv/73/8YNmyYXQ/osmXLqFKlCs2aNWPixImkp6fbrkVERNC8eXP8/f1taWFhYSQnJ3PkyBFbntDQULt7hYWF2R0Ef7OsrCySk5NtjxsPpBdCCCGEqOgcjgSHDx/OqlWrSqMut7R69WoSExPt5io+9dRT/O9//2PLli1MnDiRr7/+mqefftp2PSYmxi6ABGzPY2JiCs2TnJxMRkZGvnWZNm0anp6etkeTJk1KoolCCCGEEOWCw8PZ06ZN4+GHH2bdunU0b948zyHqM2fOLLHK3WzBggX07NmToKAgW9rIkSNtnzdv3pzAwEC6devG6dOnqVu3bqnVZeLEiYwfP972/MKFCxJICiGEEKLSKFYQuX79etuxhzcvrCktZ8+e5bfffuP7778vNF/79u0B6xnfdevWJSAggD179tjliY2NBbDNowwICLCl3ZjHw8MDZ2fnfO/j5OSEk5OT7XlycrJjDRJCVGqObAckWwEJIcoih4PIjz/+mIULF97x7W8WLVqEn58fvXv3LjRfZGQkAIGBgQCEhITwn//8h8uXL+Pn5wfAxo0b8fDwsPUchoSEsHbtWrtyNm7cSEhISAm3QgghrIq6HZBsBSSEKKscDiKdnJzo2LFjadSlQBaLhUWLFjFkyBC7vR1Pnz7N8uXL6dWrF76+vhw6dIhx48bRqVMnWrRoAUD37t1p0qQJzzzzDDNmzCAmJoa33nqL8PBwW0/iiy++yOzZs3nttdcYNmwYmzdvZuXKlfzyyy93tJ1CiMpFtgMSQpRnDo+PvPLKK/z3v/8tjboU6LfffiM6Opphw4bZpRsMBn777Te6d+9Oo0aNePXVV+nXrx8///yzLY9Wq2XNmjVotVpCQkJ4+umnefbZZ+32lQwODuaXX35h48aNtGzZko8//pj58+fL9j5CCCGEEAVwuCdyz549bN68mTVr1tC0adM8C2tuNWexOLp3746qqnnSa9Sokee0mvzUqlUrz3D1zTp37syBAweKXUchhBBCiMrE4SDSy8uLvn37lkZdhBBCCCFEOeFwELlo0aLSqIcQAuv838TExFvmS0hIgHx654UQQog7xeEgUghRehITE5m55gBGV/dC8yXEXsDZ0xfXO1QvIYQQ4mYOB5HBwcGF7gf5zz//3FaFhLjbitobCKWzf19RVuympySV6D1F2SX7SQohyiqHg8ixY8faPTeZTBw4cIB169YxYcKEkqqXEHdNUXsDZf8+cSfIfpJCiLLK4SDylVdeyTd9zpw57N2797YrJERZIPv3ibJEvh+FEGVRiY179OzZk++++66kihNCCCGEEGVYiS2s+fbbb2UYRYh8ODLHUlZdCyGEKC8cDiJbt25tt7BGVVViYmKIi4vj888/L9HKCVERFHWOJciqayGEEOWHw0Fknz597J5rNBqqVq1K586dadSoUUnVS4gKpahz2mTVtRBCiPLC4SBy8uTJpVEPIYQQQghRjshm40IUU1H375N5jkIIISqiIgeRGo2m0E3GARRFIScn57YrJUR5UNT9+2SeoxBCiIqoyEHkDz/8UOC1iIgIPvvsMywWS4lUSojyQk6XEUIIUVkVOYh87LHH8qSdOHGCN954g59//pnBgwczderUEq2cEEIIIYQom4q12fjFixcZMWIEzZs3Jycnh8jISJYsWUKtWrVKun5CCCGEEKIMciiITEpK4vXXX6devXocOXKETZs28fPPP9OsWbPSqp8QQgghhCiDijycPWPGDKZPn05AQAD/93//l+/wthBCCCGEqByKHES+8cYbODs7U69ePZYsWcKSJUvyzff999+XWOWEEEIIIUTZVOQg8tlnn73lFj9CCCGEEKJyKHIQuXjx4lKshhBCCCGEKE+KtTpbCCGEEEJUbhJECiGEEEIIh0kQKYQQQgghHCZBpBBCCCGEcJgEkUIIIYQQwmESRAohhBBCCIcVeYsfIYQQZZfFYiEhIaFIeb28vNBopA9BCHF7JIgUQogKIDMthbmbruLlW/WW+cY/3BofH587VDMhREUlQaQQQlQQRld3XD287nY1hBCVhIxnCCGEEEIIh0kQKYQQQgghHCZBpBBCCCGEcJgEkUIIIYQQwmFlOoicMmUKiqLYPRo1amS7npmZSXh4OL6+vri5udGvXz9iY2PtyoiOjqZ37964uLjg5+fHhAkTyMnJscuzdetW2rRpg5OTE/Xq1WPx4sV3onlCCCGEEOVWmQ4iAZo2bcqlS5dsjx07dtiujRs3jp9//plVq1axbds2Ll68SN++fW3XzWYzvXv3Jjs7m507d7JkyRIWL17MpEmTbHmioqLo3bs3Xbp0ITIykrFjx/L888+zfv36O9pOIYQQQojypMxv8aPT6QgICMiTnpSUxIIFC1i+fDldu3YFYNGiRTRu3Jhdu3Zx3333sWHDBo4ePcpvv/2Gv78/rVq14t133+X1119nypQpGAwGvvjiC4KDg/n4448BaNy4MTt27OCTTz4hLCzsjrZVCCGEEKK8KPM9kSdPniQoKIg6deowePBgoqOjAdi3bx8mk4nQ0FBb3kaNGlGzZk0iIiIAiIiIoHnz5vj7+9vyhIWFkZyczJEjR2x5biwjN09uGQXJysoiOTnZ9khJSSmR9gohhBBClAdlOohs3749ixcvZt26dcydO5eoqCgeeOABUlJSiImJwWAw4OXlZfcaf39/YmJiAIiJibELIHOv514rLE9ycjIZGRkF1m3atGl4enraHk2aNLnd5gohhBBClBtleji7Z8+ets9btGhB+/btqVWrFitXrsTZ2fku1gwmTpzI+PHjbc8vXLgggaQQQgghKo0y3RN5My8vLxo0aMCpU6cICAggOzubxMREuzyxsbG2OZQBAQF5VmvnPr9VHg8Pj0IDVScnJzw8PGwPd3f3222eEEIIIUS5Ua6CyNTUVE6fPk1gYCBt27ZFr9ezadMm2/UTJ04QHR1NSEgIACEhIfz1119cvnzZlmfjxo14eHjYeg1DQkLsysjNk1uGEEIIIYTIq0wHkf/617/Ytm0bZ86cYefOnTz++ONotVoGDRqEp6cnw4cPZ/z48WzZsoV9+/YxdOhQQkJCuO+++wDo3r07TZo04ZlnnuHgwYOsX7+et956i/DwcJycnAB48cUX+eeff3jttdc4fvw4n3/+OStXrmTcuHF3s+lCCCGEEGVamZ4Tef78eQYNGsTVq1epWrUq999/P7t27aJq1aoAfPLJJ2g0Gvr160dWVhZhYWF8/vnnttdrtVrWrFnDqFGjCAkJwdXVlSFDhjB16lRbnuDgYH755RfGjRvHrFmzqF69OvPnz5ftfYQQQgghClGmg8gVK1YUet1oNDJnzhzmzJlTYJ5atWqxdu3aQsvp3LkzBw4cKFYdhRCiPLFYLCQkJBQ5v5eXFxpNmR60EkLcJWU6iBRCCFGyMtNSmLvpKl6+VYuUd/zDrfHx8bkDNRNClDcSRAohRCVjdHXH1cPrbldDCFHOyRiFEEIIIYRwmASRQgghhBDCYRJECiGEEEIU0bRp07jnnntwd3fHz8+PPn36cOLECbs8mZmZhIeH4+vri5ubG/369bM72CQ+Pp5HHnkENzc3WrdunWdxb3h4OB9//PEdac/tkCBSCCGEEKKItm3bRnh4OLt27WLjxo2YTCa6d+9OWlqaLc+4ceP4+eefWbVqFdu2bePixYv07dvXdv0///kPKSkp7N+/n86dOzNixAjbtV27drF7927Gjh17J5tVLLKwRgghhBCiiNatW2f3fPHixfj5+bFv3z46depEUlISCxYsYPny5XTt2hWARYsW0bhxY3bt2sV9993HsWPHGDhwIA0aNGDkyJHMmzcPAJPJxIsvvsj8+fPRarV3vG2OkiCyhOXk5GAymQDQaDRotVrMZjMWi8WWJzc9JycHVVVt6VqtFo1GU2B6brm5dDqd7Z5FSdfr9VgsFsxmsy1NURR0Ol2B6QXV/U60SYMFRbXWSb3Waa5g4WaqqtqVc7ttyr03qgUUDYpqAa7XXUUDioIGFQ2qfR0Vxfb8hhrCDfnsyrmpTVpU651U9aa2KqiKxpauRUVBRVEt19ItKDfUMTe/kqeOSr5tyn1tQW29se7WOqr51NG+TdfraEZVtAW26eY6Xm+rfZsKrmPeNmmvfc1v1Sa7OhbwPWZNv7mO5NsmTe59Cnz/rrep8Dpeb1OeOipKnrbqNQruThq0OnBRTDeUk7dNnk4Kqg6cFXPetioaUK/n1+gtZGZmkpmZWSZ/RxQlvbz93pM2lWybFEXBYDBgMBgKbRNASkoKycnJtutOTk620+0Kk5SUBGDbCmvfvn2YTCZCQ0NteRo1akTNmjWJiIjgvvvuo2XLlmzevJnnn3+e9evX06JFCwBmzJhB586dadeu3S3vWxZIEFnCIiIicHFxAaBmzZq0bt2aQ4cOER0dbcvTsGFDGjVqxJ49e4iLi7Olt2rVilq1arF9+3ZSUlJs6SEhIfj5+bFhwwa7H7wuXbrg7OycZzP1Xr16kZGRwZYtW2xpOp2O3r17c+XKFSIiImzp7u7udO3alXPnzhEZGWlLr1q1Kh06dODkyZN2cz3uVJsiIiJoqgMyrHNIopwboFNzqJH5jy2vBQ1H8CcxMZEdO3aUaJua6iDBZCbBUBX/rPO4WK4PU8QZAkjRedPUIxMXbQZkJAJwyakGGVo3amWcsgah16RojJiA4Iy/7d6n/NpU0wv2pxlxtqQRmHXOlp6tGDjvXBd3cyJVs2MI9gbIID3LRIyxJt6mq3jnXLHlT9Z6csUpiNou2fg5Xa9jgq5Kvm1SDQauqlAtMwqDmm1Lz69Nwd7wV7oeBUuhbcqtoyUjmTMuDQtsUxWDmTquibY6pmtc822Tq4uWsyYjvtkxeJiTbOn5tSnYG6IyreFaYW0K9rYA1q/POWMdchRdvm1y1qi0cLteRwuafNvk76FwJMNoe59y5demYG+IM5lJgULbFOydYatj7vdebpvc3d1xd3dH1RrI8fTGSaMCmbZyzIoOFQWdesMfeG9vslUNWk02WtX+D3mOokdBvZ7urScmJoakpCRycnLIzr7+ddRqtTg5OWEymewCCJ1Oh8FgIDs72+5nW6/Xo9frycrKsgsIDAYDOp3OFqjmcnJyQqvVkpGRYfeH32g0oigKGRkZdnV3dnZGVVUyM6+3X1EUnJ2dMZvNZGVl2dI1Gg1Go1HaVEnapNPp8PPzIyoqKt+/T7l/J5o0aWJX18mTJzNlyhQKY7FYGDt2LB07dqRZs2YAxMTEYDAY8PLyssvr7+9PTIz198Ibb7zBqFGjqFu3LrVr12bBggWcPHmSJUuWEBERwYsvvsiGDRto164dX331FZ6enoXW425R1BvfdVFs58+fp0aNGkRFRVGtWjWg8vynVxptiouLY/7v/+Dibv3BKaiXKDUlhVGd6+Lh4VFibUpISGD+7//g7O5VaE/k1Qtn0Oid8KnqZ5d+c4/j5Yvn0OidqHItn105N7Xp6sVzqHojvlX9C+2JvHrxHIreCZ+q/oX2RF65cAatXR3z74mMu3gORW+8VsfCeyKtdXTCt2pAoT2R1+voV2hP5M11LKgnsuA65m3T1YvnQO+Ej19goT2RdnUspCcy7uJZdHZ1zL8n8srFc1Do+3e9TYXX8Xqbrl6Mtq/jtZ7IGs7Z1HRX8PWtgsHJmRxzDgoKWp3W7r65JeYy55gADVqdzi7dml25lmRNt1jMeLsY0Ov1qKpq9zOsKAqKopR6+o0/q7npADf/6SooXaPR3LW6S5vufptUVSUjI4O4uDg8PDzw87v+M5z7d+jMmTMEBwdz9OhR299vKFpP5KhRo/j111/ZsWMH1atXB2D58uUMHTrULiAGuPfee+nSpQvTp0/Pt6yuXbvyyiuvcPbsWdasWcMvv/zCiBEj8PX1LbOLbKQnsoTpdDr0er1dmlarzXduQ27AVNT0m8stTrpGo8n3CLOC0guqe2m3SafTYUFj/UN9A5W891QUpUhttVgsxMfH53u/G6WkpGBRrX/E4doQXz4sKFiDg5vqqNxcx/zz2fLf0CYzijWUUZR825qbbs2nXK+bork5HLhWtoIl3zpq8uRT8knPr0259y6wjtfadL2OWru6F7WON7fp1nW8nm6r4y3alKeO5P89RoF1tG+T5Zbv3/U2Fa2OmnzrqFEUAl1UfKv4Xd80PDvb+o9SAT9TN9ahSPkAc04Oer2uwJ/fG2m1WluAIERZ4urqiqIoXL58mYCAgDx/u3K/v93d3e06JG5l9OjRrFmzhu3bt9sCSICAgACys7NJTEy0642MjY0lICAg37IWLVqEl5cXjz32GH379qVPnz7o9Xr69+/PpEmTHGjtnSVBpKg0EhMTmbnmAEZX90LzJcRewNnTF9c7VC8hHGXQqGgVBYOTsVTvY7FYiEvJRKsrPOBUzWb8vVyKFGwKcTfkTjMzmUy3vWBFVVXGjBnDDz/8wNatWwkODra73rZtW/R6PZs2baJfv34AnDhxgujoaEJCQvKUFxcXx9SpU23Tssxms22UzmQy2Y2olTXyEy8qlaIc95aeklTodSHuvmv9mXeg50+jyX/U4UZl90+cEFYl2UseHh7O8uXL+fHHH3F3d7fNc/T09MTZ2RlPT0+GDx/O+PHj8fHxwcPDgzFjxhASEsJ9992Xp7yxY8fy6quv2obSO3bsyNdff0337t2ZN28eHTt2LLG6lzTZJ1IIIYQQoojmzp1LUlISnTt3JjAw0Pb45ptvbHk++eQTHn74Yfr160enTp0ICAjg+++/z1PW+vXrOXXqFC+99JItbfTo0dSpU4f27duTnZ3N5MmT70i7ikN6IoUQooKwWCwk3GLer8l0be7kLYaoi5rX86YVqGVd7iKKAwcO0KpVq7tdHQCOHz/Oc889R2RkJI0aNbLbVeJuqV27NmPHjrVteK0oCj/88AN9+vQpdpklUUZZUJT1yEajkTlz5jBnzpxC84WFhREWFmaX5uLiwsqVK2+rjneKBJFCCFFBJCUlsnDr8ULn/ZrNFhSFfBfSOZo3My2FkaHN8XBg+5HnnnuOJUuWMG3aNN544w1b+urVq3n88ceL9Ae6opk8eTKurq6cOHECNze3u12dfF26dAlvb+8i5Z0yZQqrV6/OEww7UoYoHySIFOVa7pY8RZGQkACV8A+UqFyMru64uHsVeN1sNl8LDG+9uMCRvI4wGo1Mnz6dF154ocIEFdnZ2RgMhmK99vTp0/Tu3ZtatWqVmTrdrKBVxXe6DFG2yJxIUa5lpqUwd9NxPt9y6taP9YfIvGnfLiHEnRcaGkpAQADTpk0rMM+UKVPyDDd/+umn1K5d2/b8ueeeo0+fPrz//vv4+/vj5eXF1KlTycnJYcKECfj4+FC9enUWLVqUp/zjx4/ToUMHjEYjzZo1Y9u2bXbXDx8+TM+ePXFzc8Pf359nnnmGK1eub37fuXNnRo8ezdixY6lSpUqeIclcFouFqVOnUr16dZycnGjVqpXdsXmKorBv3z6mTp2KoigFbm6de7/Ro0fj6elJlSpVePvtt+16bmvXrs27777Ls88+i4eHByNHjgRgx44dPPDAAzg7O1OjRg1efvllu3OeL1++zCOPPIKzszPBwcEsW7Ysz/0VRWH16tW25+fPn2fQoEH4+Pjg6upKu3bt2L17N4sXL+add97h4MGDtn0bFy9enG8Zf/31F127dsXZ2RlfX19GjhxJamqq7Xru+/vRRx8RGBiIr68v4eHhdvsLf/7559SvXx+j0Yi/vz9PPPFEvl8/UTokiBTlXu6K61s9nFzK5jCR+P/27jw+pnt94Phnsu8SIoklEiX2vZaGWi9iKaV+qmgr1lKK2sJFE9W6pLbY67YSbVEtQqq1pqKExtIkammQSy0VQRLZZJ3z+yPNNCPbDNnwvF+ved075zzne57vnDF5es75fo945ikKWVlZOr0gZ07JRYsWsXr1am7duvVUu/7555/566+/+OWXX1i+fDne3t689tpr2NnZERYWxvjx43nvvffy7WfmzJlMnz6d8PBw3N3d6devHw8ePABypgPr1q0bLVu25MyZM+zfv5+7d+/y5ptvarWxefNmTExMCA0NZcOGDQXm5+fnx7Jly1i6dCnnzp3Dw8OD/v37c+XKFSDnEm/jxo2ZPn06d+7cYcaMGYX2dfPmzRgZGXHq1Cn8/PxYvnw5X3zxhVbM0qVLad68OeHh4cyfP5/o6Gh69erFoEGDOHfuHNu3b+f48eNMmjRJs42npyc3b97kyJEj7Nixg3Xr1hEbG1toHsnJyXTu3Jnbt28TFBREZGQks2bNQq1WM2TIEKZPn07jxo25c+cOd+7cYciQIfnaSElJwcPDAzs7O06fPs3333/P4cOHtfICOHLkCNHR0Rw5coTNmzcTEBCgKUrPnDnD5MmT+fjjj4mKimL//v106tSp0LxFyZPL2UIIIZ6KPvNJ5j7ZZODAgbRo0QJvb2++/PLLJ9535cqVWbVqFQYGBtSvXx9fX19SU1P597//DcCcOXNYvHgxx48f56233tJsN2nSJM0cfuvXr2f//v18+eWXzJo1izVr1tCyZUsWLVqkid+0aRPOzs5cvnyZevXqAeDm5oavr2+R+S1duhQvLy/NvpcsWcKRI0dYuXIla9euxcnJCSMjI6ysrIq93Ovs7MyKFStQqVTUr1+f33//nRUrVjB27FhNTLdu3Zg+fbrm/ZgxYxg+fLhmgIybmxurVq2ic+fOrF+/nhs3brBv3z5OnTpFmzZtAPjyyy9p2LBhoXls3bqVe/fucfr0ac3zouvWratZb2VlhZGRUZH92bp1K2lpaXz11VdYWubMyrtmzRr69evHkiVLcHR0BMDOzo41a9ZgaGhIgwYN6Nu3L8HBwYwdO5YbN25gaWnJa6+9hrW1NS4uLrRs2bLIz1CULDkTKYQQ4qnlzidZ1Ev12HyTS5YsYfPmzVy6dOmJ99u4cWOtgT+Ojo40bdpU897Q0JAqVarkO7OWd9JnIyMjWrdurckjMjKSI0eOYGVlpXk1aNAAyLl/MdfLL79cZG6JiYn89ddf+eb569ChwxP1+ZVXXtGa79Dd3Z0rV65oTUbdunVrrW0iIyMJCAjQ6ouHhwdqtZpr165x6dIljIyMtPrSoEGDfM99zisiIoKWLVtqCsgncenSJZo3b64pICHnc1Gr1URFRWmWNW7cWGue0mrVqmmOZY8ePXBxceGll17inXfeYcuWLaSmpj5xTkJ/UkQKIYQoF506dcLDw4M5c+bkW5f7LOW88t4Ll+vxR54W9BjUgp7tXJTk5GT69etHRESE1uvKlStal0vzFkAVxeM5JScn895772n1IzIykitXrlCnTp0n2oe5uXlJpKqToo6ltbU1v/32G9u2baNatWp89NFHNG/enISEhDLL70UnRaQQQoiyoSgof79y75H85JNP+OGHHwgNDQXQLLe3tycmJkarkCzJ+RN//fVXzf/Pysri7Nmzmku4rVq14sKFC7i6ulK3bl2tlz6Fo42NDdWrV9f0LVdoaCiNGjXSO+ewsLB8fXBzcyvyiUKtWrXi4sWL+fpRt25dTExMaNCggab/uaKiooosxJo1a0ZERARxhcxJamJiUuyj+ho2bEhkZKTWAJ/Q0FDNbQm6MjIyonv37vj6+nLu3DmuX7/Ozz//rPP24ulIESmEEM+RtJQkUpMSing9/PtVVIxusWkpSXrlplarScvMJj1LTWxSOrFJ6Ti61mPg4CGsWbMGgNikdO4mpNKxY0fu3buHr68v0dHRrF27ln379pXY57R27VoCAwP5448/mDhxIvHx8YwaNQrIeaxdXFwcQ4cO5fTp00RHR3PgwAFGjhyp93OMZ86cyZIlS9i+fTtRUVHMnj2biIgIpkyZonfON27cYNq0aURFRbFt2zZWr15dbDteXl6cOHGCSZMmac6m7tmzRzOApX79+vTq1Yv33nuPsLAwzp49y5gxY4o82zh06FCcnJwYMGAAoaGh/O9//2Pnzp2cPHkSyBklfu3aNSIiIrh//z7pBcyKMXz4cMzMzBgxYgTnz5/nyJEjfPDBB7zzzjua+yGLs3fvXlatWkVERAR//vknX331FWq1Wq8iVDwdGVgjhBDPiUqVbBnXvWmRMaXxxJrsv0dd6yJn2he0zp55zfXmh8CdQM7ybHLOVK1bt45FixaxcOFCBg0axIwZM9i4caPO+yrK4sWLWbx4MREREdStW5egoCDs7e0BNGcPvby86NmzJ+np6bi4uNCrVy+dJmnPa/LkyTx8+JDp06cTGxtLo0aNCAoKws3NTe+c3333XR49ekTbtm0xNDRkypQpmml8CtOsWTOOHj3K3Llz6dixI4qiUKdOHa0R0/7+/owZM4bOnTvj6OjIJ598wvz58wtt08TEhIMHDzJ9+nT69OlDVlYWjRo10jydZdCgQezatYuuXbuSkJCAv78/np6eWm1YWFhw4MABpkyZQps2bbCwsGDQoEEsX75c58/D1taWXbt24ePjQ1paGm5ubmzbto3GjRvr3IZ4OirlRXw8QCm4desWzs7O3Lx5k5o1a5Z3Os+8uLg41h25iqWNbZFx927/iYGxKVUcip/EVtfY8oqTHCVHXePMDbJpaZdJjVouGJuYApCZ8XfBZ1x0cahrXGm0qWtcdnY2DtamGBnJeY5cXbp0oUWLFqxcubK8U3kmpaWlce3aNWrXro2ZmZnWOvn7/eTkcrYQQgghhNCbFJFCCCGEEEJvcq1ACCGEqOBCQkLKOwUh8pEzkUIIIYQQQm9yJlKUKbVardNEsPHx8SBjvoQQQogKS4pIUaYSEhJYvjccM0vrIuPi797GvFIVKt7zIIQQQggBUkSKcmBmaV3s1D2pSQ/LJhkhRMXz9xNtdGVoaKj1TGkhRNmo0PdE/uc//6FNmzZYW1vj4ODAgAEDtB7MDjlzZ+VMXvvPa/z48VoxN27coG/fvlhYWODg4MDMmTPz/UCFhITQqlUrTE1NqVu3LgEBAaXdPSGEEAVQq9XcS0rTPNWmqNfdhFS9nyIjhCgZFbqIPHr0KBMnTuTXX3/l0KFDZGZm0rNnT61nbQKMHTuWO3fuaF6+vr6addnZ2fTt25eMjAxOnDjB5s2bCQgI4KOPPtLEXLt2jb59+9K1a1ciIiKYOnUqY8aM4cCBA2XWVyGEEP8wMDDE0LD4l6qI50YLIUpXhb6cvX//fq33AQEBODg4cPbsWTp16qRZbmFhgZNTwU99OHjwIBcvXuTw4cM4OjrSokULFi5ciJeXFz4+PpiYmLBhwwZq167NsmXLgJzHbR0/fpwVK1bg4eFReh0UQghRoahUKgIDAxkwYEB5p/JUfHx82L17NxEREeWdiniOVegzkY97+DDnPrnKlStrLd+yZQv29vY0adKEOXPmkJqaqll38uRJmjZtqvVAdw8PDxITE7lw4YImpnv37lptenh4aB4mX5D09HQSExM1r6SkpKfunxBCvCjOnPqV6naWDB88UO9tWzetz8Z1q0shq9LTpUsXpk6dWiptq1Qqdu/erbVsxowZBAcHl8r+hMhVoc9E5qVWq5k6dSodOnSgSZMmmuXDhg3DxcWF6tWrc+7cOby8vIiKimLXrl0AxMTEaBWQgOZ9TExMkTGJiYk8evQIc3PzfPn85z//YcGCBSXaRyGEeFFs/Wozo9+bwNavNxNz5y+cqlUv75SeSEZGBiYmJuWdRj5WVlZYWVmVdxriOffMnImcOHEi58+f59tvv9VaPm7cODw8PGjatCnDhw/nq6++IjAwkOjo6FLNZ86cOTx8+FDzunjxYqnuTwghiqIoCo8yssvlpeg5p2tKcjJ7AncwYvQ4uvfsxfYt3+SLObR/Hx5dOuDiYEuj2jUZOfxNAAb27cmtGzf4aM4snCqZU6NyTqHk4+NDixYttNpYuXIlrq6umvenT5+mR48e2NvbU6lSJTp37sxvv/2mV+5dunRh0qRJTJ06FXt7e80tT+fPn6d3795YWVnh6OjIO++8w/379wHw9PTk6NGj+Pn5aQaAXr9+vdjtcvc3efJkZs2aReXKlXFycsLHx0ezPrd/AwcORKVSad4//nmo1Wo+/vhjatasiampKS1atNC6Zez69euoVCp27dpF165dsbCwoHnz5kVekRPimTgTOWnSJPbu3csvv/xCzZo1i4xt164dAFevXqVOnTo4OTlx6tQprZi7d+8CaO6jdHJy0izLG2NjY1PgWUgAU1NTTE1NNe8TExP165QQQpSgtEw1PVadKJd9H5rcHgtT3c9J7AncSV23etR1q8egIUP5aPZMJk+fqZmmJ/jgAcaMGMaUGV6s/vxLMjMyCD6YM9Bx09ff8q9X2/K252jeHjGS7Gy1zvtNSkpixIgRrF69GkVRWLZsGX369OHKlStYWxc9d21emzdvZsKECYSGhgI5899269aNMWPGsGLFCh49eoSXlxdvvvkmP//8M35+fly+fJkmTZrw8ccfA1C1atVit8u7v2nTphEWFsbJkyfx9PSkQ4cO9OjRg9OnT+Pg4IC/vz+9evXCsJCBRn5+fixbtozPP/+cli1bsmnTJvr378+FCxdwc3PTxM2dO5elS5fi5ubG3LlzGTp0KFevXsXI6JkoF0QZq9DfCkVR+OCDDwgMDCQkJITatWsXu03uTcTVqlUDwN3dnU8//ZTY2FgcHBwAOHToEDY2NjRq1EgT89NPP2m1c+jQIdzd3UuwN0IIIQC2fR3A/w0ZCkC37j2ZmvgeJ44fo0PHnAGTq1cupf/AQcz693zNNo2bNgPArnJlDAwNsbKywsHRSa/pfbp166b1fuPGjdja2nL06FFee+01ndtxc3PTmgXkk08+oWXLlixatEizbNOmTTg7O3P58mXq1auHiYlJvkGga9asKXY7gGbNmuHt7a3Z95o1awgODqZHjx5UrVoVAFtb20IHmAIsXboULy8v3nrrLQCWLFnCkSNHWLlyJWvXrtXEzZgxg759+wKwYMECGjduzNWrV2nQoIHOn494cVToInLixIls3bqVPXv2YG1trbmHsVKlSpibmxMdHc3WrVvp06cPVapU4dy5c3z44Yd06tSJZs1yfnB69uxJo0aNeOedd/D19SUmJoZ58+YxceJEzZnE8ePHs2bNGmbNmsWoUaP4+eef+e677/jxxx/Lre9CCKEPM2MDDk/pgJGxcZFxmRkZqFSqYuP0iTVUdJ8YPPrKFcLPnmHTlu0AGBkZ8fobg9j2dYCmiLxw/neGveOpc5u6unv3LvPmzSMkJITY2Fiys7NJTU3lxo0berXz8ssva72PjIzkyJEjBd6DGB0drSkGH6frdrl/z3JVq1aN2NhYnfNNTEzkr7/+okOHDlrLO3ToQGRkpNayvPvKPRkTGxsrRaQoUIUuItevXw/k3BOSl7+/P56enpiYmHD48GFWrlxJSkoKzs7ODBo0iHnz5mliDQ0N2bt3LxMmTMDd3R1LS0tGjBihuaQAULt2bX788Uc+/PBD/Pz8qFmzJl988YVM7yOEeGaoVCrMTQwwMi563kQjDP8uDIufX1HX2MwM3c8GbtvyFVlZWbSo/5JmmaIomJqasuizFdhUqoSZmZnO7eV9uo1ardZ6kER6ejqAZtmIESN48OABfn5+uLi4YGpqiru7OxkZGbrvD7C01H4ga3JyMv369WPJkiX5YnMLsYLoup3xY0W8SqVCrdb9Mr4+8u4r9/aC0tqXePZV6CKyuJu1nZ2dOXr0aLHtuLi45Ltc/bguXboQHh6uV35CCCF0l5WVxc7vtuHz6WI6d9OeVm3ksDcJ3PEdI0aPpWGjJhz/JYThI0YW2I6JsYnmMnbu021MrW25ExPD3cQ0TfETduY3stUKsUnpKNnZhIaGsm7dOvr06QPAzZs3tQaxPKlWrVqxc+dOXF1dC7130MTEJN+ld12204WxsXGRl/VtbGyoXr06oaGhdO7cWbM8NDSUtm3bPvF+hXhmRmcLIYR4th0+uJ+HCQkMe8eTho0aa7369h/A1q8DAPhwphd7du3Ad9FCLkf9waUL51m9YqmmHedaLvx6IpQ7f90m7sEDDAwMebVTFx7cv8/61Su5+eefbN70X44cPgQqNE+2qVu3Ll9//TWXLl0iLCyM4cOHFzp4Uh8TJ04kLi6OoUOHcvr0aaKjozlw4AAjR47UFHeurq6EhYVx/fp17t+/j1qt1mk7Xbi6uhIcHExMTAzx8fEFxsycOZMlS5awfft2oqKimD17NhEREUyZMuWp+y9eXFJEihKhVquJi4sr9hUfHw96TgcihHg+bN/yNa926oJNpUr51vV9fQCR4b9x8fzvuHfoyIZNX3Hwpx/p/mo7BvXrTcTZM5rYWXPnc/PGn7zSojHNG+RcFq9XvwGLl/kR8MXndHu1LeFnzzDhg6la+9i4cSPx8fG0atWKd955h8mTJ2sGXD6N3LN82dnZ9OzZk6ZNmzJ16lRsbW0xMMj5MztjxgwMDQ1p1KgRVatW5caNGzptp4tly5Zx6NAhnJ2dadmyZYExkydPZtq0aUyfPp2mTZuyf/9+goKCtEZmC6EvlaLvBF+iQLdu3cLZ2ZmbN28WOw3R8yguLo7le8Mxsyx6moz4u7cxr1SFKg6FjyIEuHf7TwyMTUssrjTalBwlx/LK0dwgm5Z2mdSo5YKxSc4AQV0HwZTGwJryitMnNjs7GwdrU5mq5gWVlpbGtWvXqF27dr57bl/0v99PQ/41iRJjZmmNpY1tkTGpSQ/LJhkhhBBClCq5nC2EEEIIIfQmZyJFodRqNQkJCTrFyr2OQgghxItFikhRqISEBJ3uc4R/7nW0LDZSCCGEEM8DKSJFkXS5zxHkXkchRAWXZ1Ly4hgaGmrmmhRCFE6KSCGEEM+93EnJDY2KHsWtZGfjaGsho7iF0IH8KxFCCPFCMDAwxNCw6Ec46j7FtxBCRmcLIYQQQgi9SREphBBCCCH0JkWkEEKI59LkCWPxHDZY8/7/+vdh/uwZZZ5HSEgIKpVK5ynTKrIuXbowderU8k5DVBBSRAohhCgzH04aj1Mlc5wqmeNsb8MrLRqzbMkinUdOP43/bv4Gr7neOsU+K4WfSqVi9+7dJd5uYf3ftWsXCxcuLPH9iWeTDKwRQghRprp274nfus9JT08n+OAB5syYirGRMZOnz8wXm5GRgYmJSYns186ucvHP4/57KqDs7JwhNllZWYUWuKU5FVBmZibGOjw7vKxVrly5vFMQFYiciRRCiOeBoqDKTIXMlCJfqsxUneL0itXzaVWmpiY4ODrhXMsFzzHj6NSlGwf27QVyLkGPfncYq5Z/RvP6tenwcjMAbt+6ydgRw6lXy4kGLtUZMXQwN/78U9NmdnY23v+eRb1aTjR0rcHH8/+N8lhej1/OTk9PZ+FHc2nVqC61qlbilRaN2fpVAL9duEz37t0BqFq1KsbGxgx715PYpHRiHj5i/sef8lKdOlhYWNC8eXN27NihtZ+ffvqJevXqYW5uTteuXbl+/Xqxn4lKpWL9+vX0798fS0tLPv30UwD27NlDq1atMDMz46WXXmLBggWaotbV1RWAgQMHolKpNO+L2y53f1988QUDBw7EwsICNzc3goKCALh+/Tpdu3YFwM7ODpVKhaenJ5D/cnZ8fDzvvvsudnZ2WFhY0Lt3b65cuaJZHxAQgK2tLQcOHKBhw4ZYWVnRq1cv7ty5U+xnIio+ORMphBDPAVXWI6p/Xr9c9n37vSgwefLnVZmZmxMfF6d5H/rLUaytrdm++0cg56zcW2/0p3WbduzZdxhDIyNWfraYYYP6cyAkFFNTU9avXsn2Ld+wYs0G3Oo3YMNqP/btDeLVTp0L3e8H743m7OkwPlmyjMZNmnHjz+vE3o2hpnMtvvx6G6PfGUro2XNYW1tjZmaOoaEhq5cvZed321i8bBWtmzXkxIkTvP3221StWpXOnTtz8+ZN3njjDSZOnMi4ceM4c+YM06dP1+lz8PHxYfHixaxcuRIjIyOOHTvGu+++y6pVq+jYsSPR0dGMGzcOAG9vb06fPo2DgwP+/v706tVLM31RcdvlWrBgAb6+vnz22WesXr2a4cOH8+eff+Ls7MzOnTsZNGgQUVFR2NjYYG5uXmDOnp6eXLlyhaCgIGxsbPDy8qJPnz5cvHhRcyY1NTWVpUuX8vXXX2NgYMDbb7/NjBkz2LJli06fi6i4pIgUQghRLhRF4VjIEUKCDzFq3ATNcgsLCz5buQYLy5zCdMf2bShqNcvXrNdcPl65biP1azlxMvQYXbp157/r1/DBtBn07T8AAN+Vqwn5+VCh+46+eoWgwJ18t/tHOnXtBoBL7dpkZmSgUqmwtcu5bGtvX5VKtrZAzplLv+W+fL/nR1q+3AYHa1Pq1avH8ePH+fzzz+ncuTPr16+nTp06LFu2DID69evz+++/s2TJkmI/j2HDhjFy5EjN+1GjRjF79mxGjBgBwEsvvcTChQuZNWsW3t7eVK1aFQBbW1ucnJw02y1YsKDI7XJ5enoydOhQABYtWsSqVas4deoUvXr10ly2dnBwwPbv/j8ut3gMDQ2lffv2AGzZsgVnZ2d2797N4ME5g5oyMzPZsGEDderUAWDSpEl8/PHHxX4eouKTIlIIIZ4DipE5f42/jKFx0T/rWRmZqFSqYuP0iVXURuhzZ+Ch/ft4qbo9WZmZqNVqBg4ewow58zTrGzRqpHUf5IXfz3Htf9HUqVFVq520tDT+vH6NxMSH3I2JoVXrtpp1RkZGNG/ZKt8l7Vznz0ViaGiI+6sddc772v+ieZSaypsDXgPQ9DkjI4MWLVqQlZXFxYsXadOmjdal47Zt2xbQWn6tW7fWeh8ZGUloaKjm0jbkXLZPS0sjNTUVCwuLAtvRdbtmzZpp1ltaWmJjY0NsbKxOuQJcunQJIyMj2rVrp1lWpUoV6tevz6VLlzTLLCwsNAUkQLVq1fTaj6i4pIgUQojngUqFYmwBxQzGUJQMUKmKjdMrNiNDn0zp0LEzS5avwtjEGKdq1fM9YtDcQvvSeEpKCs1atGTdfwPytWVTyUavfWv2Ucjl2aKkpiQD8M13gdjb24OiYPB37iYmpsQmpZOelU1apprYpHTNdgkp6QW29zhLS+1+Jycns2DBAt544418sWZmZoW2o+t2jw/cUalUqNVqnXLVR0H7Kay4F88WKSKFEEKUKQtLC2rnOTNVnGbNWxC0awf2VatibaNdNOZefnZ0cuK3M6dw7/AqkDOq+lxEOE2btyiwzQaNmqBWqzl5/JjmcnZeJiY5hU+2+p8HIdar3xBTU1Nu37pJm7btUKlU+UZ716vfkAP7ftR6vGL4b2d17mterVq1Iioqirp16xYaY2xsrBlJrs92xck9E/x423k1bNiQrKwswsLCNJezHzx4QFRUFI0aNXrifYtnh4zOFkIIUaG98eZbVK5ShRHDBvPrieP8ef06ocd+Ye6sadz56zYAY8ZPZM2KZezbG8SVy1HMnjaFhw8fFtpmLRcX3hz2Nh9Oeo99e4M0bf6wexcANZ1roVKpOLR/H/fv3yMlORkra2smfDAV7zmz+P7brVy/9j/ORYTzxefr2L71GwDeHTWGa9FXWTBvDlevXGbX99/y3bZvnqjfH330EV999RULFizgwoULXLp0iW+//ZZ58/659O/q6kpwcDAxMTHEx8frvF1xXFxcUKlU7N27l3v37pGcnJwvxs3Njddff52xY8dy/PhxIiMjefvtt6lRowavv/76E/VZPFukiBRCCFGhWVhYsHvfIWrUdGbU20Pp1LYF0yaNJz0tHStrawAmfDCV/3trKJMnjOW17l2wtLai92v9i2x3yfJVvPb6QGZPn0LHNs2ZMfl9UlNTAahWvQYz/z2fT33m07SuC3NmfgiA1zxvPpw1m7V+y+navg1DB71O8IH91HJxBXKKzy++3sb+H3/gXx3a8tWmL5g9z+eJ+u3h4cHevXs5ePAgbdq04ZVXXmHFihW4uLhoYpYtW8ahQ4dwdnamZcuWOm9XnBo1amgG6Dg6OjJp0qQC4/z9/Xn55Zd57bXXcHd3R1EUfvrppwo5x6UoeSpFbkwoEbdu3cLZ2ZmbN29Ss2bN8k6nRMTFxbHuyFUsbWyLjb13+08MjE2p4uBUIeMkR8mxIsU9bZvmBtm0tMukRi0XjE1MgX8u6xY3mbaucaXR5ouaY3Z2Ng7Wpvnu/RRlJy0tjWvXrlG7du1895M+j3+/y4qciRRCCCGEEHqT/ywSQgghStPfj1LURWk+SlGIkiZFpBBCCFGK1Go195LSMDQqZvql7GwcbS3ksrd4Zsg3VQghhChlBgaGWtP+FKTwyXSEqJjknkghhHjm/H25U8ZFCqETGUNcOuRM5HNErVaTkJCgU6ytrS0GBvLfEEI8izLUKrIVhYz0NIxNC39yiRAiR+7UTTL1UMmSIvI5kpCQwPK94ZhZWhcZ9yjpIaM6voSdnV2RcfHx8XKmQ4gKKBsVf6UaYHz/HgAmpmZkZWWhQoWiFP3YuqzMTJ3i9Iktr7jnLUd1VhYpBlnFXvYGGYCjK0VRSE1NJTY2FltbW50+W6E7KSKfM2aW1sXO65ia9JD1wX9gW6VqkXHxd29jXqkKlkVGCSHKw800EyCDzOy7GKpUZGdnoVKpMDAo5r47HeP0iS2vuOcxx1sKGBgWfZVIUSvYWJjI1SQ92Nra4uRU/PytQj9SRL6gdC02hRAVlYqbaab8laZgYqAQH3sPlZEJtpXti9xK1zh9Yssr7kXN8VFKEkPbVsPW1rbYfYucS9hyBrJ0SBEphBDPsGxUPFKrSEpXY6BWMFUX/cdS1zh9Yssr7kXNMSUTTExM8j15RYiyJufCH7N27VpcXV0xMzOjXbt2nDp1qrxTEkIIITTUajXx8fHExcUV+1Kri79nUzyZouqFadOmUblyZZydndmyZYvWdt9//z39+vUr63RLhZyJzGP79u1MmzaNDRs20K5dO1auXImHhwdRUVE4ODiUW166jrqWgTBCCPH8S0tJYn3wg2Lva9d1EGUumbVDd0XVC2FhYWzdupWDBw9y5coVRo0ahYeHB/b29jx8+JC5c+dy+PDh8u5CiZAiMo/ly5czduxYRo4cCcCGDRv48ccf2bRpE7Nnzy63vHQddS0DYYQQ4sVQkoMoQb+CU4rNousFAwMDunTpQuvWrWndujVTp07l2rVr2NvbM2vWLCZMmECtWrXKuQclQ4rIv2VkZHD27FnmzJmjWWZgYED37t05efJkvvj09HTS09M17x8+zBmEcuvWLc0zUlUqFYaGhmRnZ2tNdGpgYICBgUGhyx9/xmpiYiKJcffITEvRWp57kSL3n3JKwn1SkxNJT0nQWv5PvApQSLp/F5WRCZl/x6lzJp/g8cki4u/dxSBPHIACKAXEJ9y7C4/F5o03IKefD//ed06O/yx/vE/5c9Tua1E5Ftanh/fvohiakJGSoLX88T7l5piRkqCV++PxSfdjCsgxf3z8Pe2+PN7XvH3KzTE9JaHA4/d4jpkpCQX2tbAcHz8euQo7fgX1qegc/+nT4zmCUmCfEu/FYGCsve8nOX6P54ihCekpDwv5juXEa+f4T+6Pxz+8dxfDAnJ8vE+F5VhQnx7ez/nM01MeFnr8Hs+xsONXeI75+1Tcdyxvn/7595o/x7x9Siwgx4L6lHDvbr5j/WTHjwJyTMjX17x9KunfvaJyrCi/e7nfMRXqQr9jeX9T/rPlCuZWtiiAgQqteLWSs01WeirDOtTDxsYmJzcDA1QqFdnZ2s/byS0yH7+cXthyQ0NDFEUpcLlardb8rbS1tdX8bVWr1VrxT/o399atWzmf18OHmn4BmJqaYmpqyuOKqxfef/99Nm7cSHx8PP/73/949OgRdevW5fjx4/z222+sW7cuX5vPLEUoiqIot2/fVgDlxIkTWstnzpyptG3bNl+8t7d3zr9BeclLXvKSl7zk9dy9vL29n7he8Pb2VurUqaM0adJE2bVrl5Kenq40adJEOXPmjLJ69WqlXr16Svv27ZXz58+XTBFTTuRM5BOaM2cO06ZN07zPysri0qVLODs763yav0uXLoSEhDxxDvpur098cbFPur6g5UlJSTRq1IiLFy9ibV30Jfvy8LTHqTTbfpLtdd1Gl7iiYvRd96J+D0qi3dL6LXja70BR6+U7ULJty2/Bk1Or1dy4cYNGjRphZPRPWVTQWUhd+fj44OPjo3m/YMECunfvjrGxMZ988gm///47e/fu5d133+Xs2bNPk365kiLyb/b29hgaGnL37l2t5Xfv3i1wgtKCTnN36NBBr32amJhQs2ZN/ZN9wu31iS8u9knXF7Q8MTERgBo1amhdSqgonvY4lWbbT7K9rtvoEldUjL7rXtTvQUm0W1q/BU/7HShqvXwHSrZt+S14Ovrco6hvvfDHH3/wzTffEB4ezqZNm+jUqRNVq1blzTffZNSoUSQlJVXI/2jSxYt9Z2weJiYmvPzyywQHB2uWqdVqgoODcXd3L5V9Tpw4sUy31ye+uNgnXf+0fS4PpZlzWX8H9NlGl7iiYp50XUVVWjmXRLul9VvwtN+BotbLd6Bk25bfgrKjT72gKArvvfcey5cvx8rKiuzsbDIzMwE0//v4/aTPEpWiyJwwubZv386IESP4/PPPadu2LStXruS7777jjz/+wNHRsbzTe24lJiZSqVKlfDc1ixeLfA+EfAcEPBvfA13rhf/+978cOHCAHTt2AHDq1Cl69OjBgQMH2LdvHzt27ODChQvl1Y2nJpez8xgyZAj37t3jo48+IiYmhhYtWrB//34pIEuZqakp3t7eT3X/iXj2yfdAyHdAwLPxPdClXrh79y6ffvopJ06c0Cxr27Yt06dPp2/fvjg4OLB58+bySL/EyJlIIYQQQgihN7knUgghhBBC6E2KSCGEEEIIoTcpIoUQQgghhN6kiBRCCCGEEHqTIlKUm19++YV+/fpRvXp1VCoVu3fvLu+URCkr7pgrisJHH31EtWrVMDc3p3v37ly5cqV8khUlpiSOe1xcHMOHD8fGxgZbW1tGjx5NcnJyGfZC6KOsjvm5c+fo2LEjZmZmODs74+vrW9pdE3lIESnKTUpKCs2bN2ft2rXlnYooI8Udc19fX1atWsWGDRsICwvD0tISDw8P0tLSyjhTUZJK4rgPHz6cCxcucOjQIfbu3csvv/zCuHHjyqoLQk9lccwTExPp2bMnLi4unD17ls8++wwfHx82btxY6v0Tfyu/x3YL8Q9ACQwMLO80RBl6/Jir1WrFyclJ+eyzzzTLEhISFFNTU2Xbtm3lkKEoDU9y3C9evKgAyunTpzUx+/btU1QqlXL79u0yy108mdI65uvWrVPs7OyU9PR0TYyXl5dSv379Uu6RyCVnIoUQFcK1a9eIiYmhe/fummWVKlWiXbt2nDx5shwzE6VJl+N+8uRJbG1tad26tSame/fuGBgYEBYWVuY5i6dTUsf85MmTdOrUCRMTE02Mh4cHUVFRxMfHl1FvXmxSRAohKoSYmBiAfE+IcnR01KwTzx9djntMTAwODg5a642MjKhcubJ8N55BJXXMY2JiCmwj7z5E6ZIiUgghhBBC6E2KSCFEheDk5ATkPG82r7t372rWieePLsfdycmJ2NhYrfVZWVnExcXJd+MZVFLH3MnJqcA28u5DlC4pIoUQFULt2rVxcnIiODhYsywxMZGwsDDc3d3LMTNRmnQ57u7u7iQkJHD27FlNzM8//4xaraZdu3ZlnrN4OiV1zN3d3fnll1/IzMzUxBw6dIj69etjZ2dXRr15sRmVdwLixZWcnMzVq1c1769du0ZERASVK1emVq1a5ZiZKC3FHfOpU6fyySef4ObmRu3atZk/fz7Vq1dnwIAB5Ze0eGpPe9wbNmxIr169GDt2LBs2bCAzM5NJkybx1ltvUb169XLqlShKWRzzYcOGsWDBAkaPHo2Xlxfnz5/Hz8+PFStWlEeXX0zlPTxcvLiOHDmiAPleI0aMKO/URCkp7pir1Wpl/vz5iqOjo2Jqaqr861//UqKioso3afHUSuK4P3jwQBk6dKhiZWWl2NjYKCNHjlSSkpLKoTdCF2V1zCMjI5VXX31VMTU1VWrUqKEsXry4rLooFEVRKYqilG3ZKoQQQgghnnVyT6QQQgghhNCbFJFCCCGEEEJvUkQKIYQQQgi9SREphBBCCCH0JkWkEEIIIYTQmxSRQgghhBBCb1JECiGEEEIIvUkRKYR4Jrm6urJy5coiY1QqFbt37wbg+vXrqFQqIiIiAAgJCUGlUpGQkFAq+c2fP59x48YVGdOlSxemTp1aKvsvyCuvvMLOnTvLbH9CiOebFJFCiFJz7949JkyYQK1atTA1NcXJyQkPDw9CQ0M1MXkLvZJ2584devfuXeC69u3bc+fOHSpVqgRAQEAAtra2JbLfmJgY/Pz8mDt3bom0V1LmzZvH7NmzUavV5Z2KEOI5IEWkEKLUDBo0iPDwcDZv3szly5cJCgqiS5cuPHjwoEz27+TkhKmpaYHrTExMcHJyQqVSlfh+v/jiC9q3b4+Li0uJt/00evfuTVJSEvv27SvvVIQQzwEpIoUQpSIhIYFjx46xZMkSunbtiouLC23btmXOnDn0798fyLkkDTBw4EBUKpXmfXR0NK+//jqOjo5YWVnRpk0bDh8+nG8fSUlJDB06FEtLS2rUqMHatWu11hd1ljPv5eyQkBBGjhzJw4cPUalUqFQqfHx8+Pjjj2nSpEm+bVu0aMH8+fML7fu3335Lv379tJalpKTw7rvvYmVlRbVq1Vi2bFm+7b7++mtat26NtbU1Tk5ODBs2jNjYWAAURaFu3bosXbpUa5uIiAhUKhVXr15FURR8fHw0Z36rV6/O5MmTNbGGhob06dOHb7/9ttDchRBCV1JECiFKhZWVFVZWVuzevZv09PQCY06fPg2Av78/d+7c0bxPTk6mT58+BAcHEx4eTq9evejXrx83btzQ2v6zzz6jefPmhIeHM3v2bKZMmcKhQ4f0zrV9+/asXLkSGxsb7ty5w507d5gxYwajRo3i0qVLmrwAwsPDOXfuHCNHjiywrbi4OC5evEjr1q21ls+cOZOjR4+yZ88eDh48SEhICL/99ptWTGZmJgsXLiQyMpLdu3dz/fp1PD09gZyCeNSoUfj7+2tt4+/vT6dOnahbty47d+5kxYoVfP7551y5coXdu3fTtGlTrfi2bdty7NgxvT8jIYTIRxFCiFKyY8cOxc7OTjEzM1Pat2+vzJkzR4mMjNSKAZTAwMBi22rcuLGyevVqzXsXFxelV69eWjFDhgxRevfuXWDb165dUwAlPDxcURRFOXLkiAIo8fHxiqIoir+/v1KpUqV8++3du7cyYcIEzfsPPvhA6dKlS6F5hoeHK4By48YNzbKkpCTFxMRE+e677zTLHjx4oJibmytTpkwptK3Tp08rgJKUlKQoiqLcvn1bMTQ0VMLCwhRFUZSMjAzF3t5eCQgIUBRFUZYtW6bUq1dPycjIKLTNPXv2KAYGBkp2dnahMUIIoQs5EymEKDWDBg3ir7/+IigoiF69ehESEkKrVq0ICAgocrvk5GRmzJhBw4YNsbW1xcrKikuXLuU7E+nu7p7v/aVLl0q0D2PHjmXbtm2kpaWRkZHB1q1bGTVqVKHxjx49AsDMzEyzLDo6moyMDNq1a6dZVrlyZerXr6+17dmzZ+nXrx+1atXC2tqazp07A2j6Xb16dfr27cumTZsA+OGHH0hPT2fw4MEADB48mEePHvHSSy8xduxYAgMDycrK0tqHubk5arW60LPDQgihKykihRClyszMjB49ejB//nxOnDiBp6cn3t7eRW4zY8YMAgMDWbRoEceOHSMiIoKmTZuSkZFRRln/o1+/fpiamhIYGMgPP/xAZmYm//d//1dovL29PQDx8fF67SclJQUPDw9sbGzYsmULp0+fJjAwEECr32PGjOHbb7/l0aNH+Pv7M2TIECwsLABwdnYmKiqKdevWYW5uzvvvv0+nTp3IzMzUbB8XF4elpSXm5uZ65SeEEI+TIlIIUaYaNWpESkqK5r2xsTHZ2dlaMaGhoXh6ejJw4ECaNm2Kk5MT169fz9fWr7/+mu99w4YNnygvExOTfHkAGBkZMWLECPz9/fH39+ett94qsgCrU6cONjY2XLx4UWuZsbExYWFhmmXx8fFcvnxZ8/6PP/7gwYMHLF68mI4dO9KgQQPNoJq8+vTpg6WlJevXr2f//v35zoqam5vTr18/Vq1aRUhICCdPnuT333/XrD9//jwtW7bU7UMRQogiGJV3AkKI59ODBw8YPHgwo0aNolmzZlhbW3PmzBl8fX15/fXXNXGurq4EBwfToUMHTE1NsbOzw83NjV27dtGvXz9UKhXz588vcG7D0NBQfH19GTBgAIcOHeL777/nxx9/fKJ8XV1dSU5OJjg4mObNm2NhYaE5wzdmzBhNcZp3jsuCGBgY0L17d44fP86AAQOAnEFGo0ePZubMmVSpUgUHBwfmzp2LgcE//x1fq1YtTExMWL16NePHj+f8+fMsXLgwX/uGhoZ4enoyZ84c3NzctC7pBwQEkJ2dTbt27bCwsOCbb77B3Nxca6qhY8eO0bNnzyf6jIQQQkt535QphHg+paWlKbNnz1ZatWqlVKpUSbGwsFDq16+vzJs3T0lNTdXEBQUFKXXr1lWMjIwUFxcXRVFyBsF07dpVMTc3V5ydnZU1a9YonTt31hqE4uLioixYsEAZPHiwYmFhoTg5OSl+fn5aOaDHwBpFUZTx48crVapUUQDF29tbq62OHTsqjRs31qnvP/30k1KjRg2twStJSUnK22+/rVhYWCiOjo6Kr69vvj5t3bpVcXV1VUxNTRV3d3clKChIK+dc0dHRCqD4+vpqLQ8MDFTatWun2NjYKJaWlsorr7yiHD58WLP+1q1birGxsXLz5k2d+iGEEEVRKYqilGcRK4QQFZ2iKLi5ufH+++8zbdo0neLbtWvHhx9+yNChQ0s8n2PHjvGvf/2Lmzdv4ujoqPN2Xl5exMfHs3HjxhLPSQjx4pF7IoUQogj37t1jzZo1xMTEFDo35ONUKhUbN27MNzL6aaWnp3Pr1i18fHwYPHiwXgUkgIODQ4GXyIUQ4knImUghhCiCSqXC3t4ePz8/hg0bVq65BAQEMHr0aFq0aEFQUBA1atQo13yEEC82KSKFEEIIIYTe5HK2EEIIIYTQmxSRQgghhBBCb1JECiGEEEIIvUkRKYQQQggh9CZFpBBCCCGE0JsUkUIIIYQQQm9SRAohhBBCCL1JESmEEEIIIfQmRaQQQgghhNDb/wPheZtq3z3JQwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "}\n",
       "#T_1811e_row1_col9 {\n",
       "  background-color: #ff5151;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row2_col8, #T_1811e_row9_col3, #T_1811e_row11_col8, #T_1811e_row14_col3 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row2_col9, #T_1811e_row12_col9, #T_1811e_row15_col3, #T_1811e_row15_col8 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row3_col6 {\n",
       "  background-color: #ff8989;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row3_col7, #T_1811e_row14_col7 {\n",
       "  background-color: #ffa5a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row3_col8, #T_1811e_row19_col8 {\n",
       "  background-color: #6d6dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row3_col9, #T_1811e_row8_col4, #T_1811e_row10_col8, #T_1811e_row12_col7, #T_1811e_row23_col2 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row4_col6, #T_1811e_row11_col7, #T_1811e_row15_col5, #T_1811e_row17_col9 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row4_col7 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row4_col9, #T_1811e_row8_col6, #T_1811e_row11_col2 {\n",
       "  background-color: #d1d1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row5_col5 {\n",
       "  background-color: #fc0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row5_col6, #T_1811e_row6_col7, #T_1811e_row9_col9, #T_1811e_row10_col1, #T_1811e_row11_col9, #T_1811e_row12_col4, #T_1811e_row18_col5 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row5_col7, #T_1811e_row9_col7, #T_1811e_row10_col3, #T_1811e_row15_col0 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row5_col8, #T_1811e_row12_col1 {\n",
       "  background-color: #a1a1ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row5_col9, #T_1811e_row7_col7, #T_1811e_row9_col5, #T_1811e_row17_col2 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row6_col5, #T_1811e_row16_col9, #T_1811e_row18_col8, #T_1811e_row21_col1 {\n",
       "  background-color: #ff8d8d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row6_col6, #T_1811e_row9_col4, #T_1811e_row21_col2 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row6_col8, #T_1811e_row11_col3, #T_1811e_row16_col8, #T_1811e_row19_col5, #T_1811e_row22_col6 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row6_col9, #T_1811e_row19_col3 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row7_col4, #T_1811e_row12_col2, #T_1811e_row18_col3 {\n",
       "  background-color: #c9c9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row7_col5, #T_1811e_row7_col9, #T_1811e_row10_col5 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row7_col6, #T_1811e_row14_col1, #T_1811e_row19_col2 {\n",
       "  background-color: #c1c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row7_col8, #T_1811e_row15_col1 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row8_col3 {\n",
       "  background-color: #6969ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row8_col5, #T_1811e_row13_col2 {\n",
       "  background-color: #d5d5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row8_col7, #T_1811e_row8_col9, #T_1811e_row11_col5, #T_1811e_row16_col5 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row8_col8, #T_1811e_row15_col4, #T_1811e_row17_col1, #T_1811e_row18_col1, #T_1811e_row20_col4 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row9_col2, #T_1811e_row15_col6 {\n",
       "  background-color: #ffa9a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row9_col6, #T_1811e_row10_col6 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row9_col8, #T_1811e_row14_col8, #T_1811e_row17_col8 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row10_col2, #T_1811e_row17_col6, #T_1811e_row23_col0 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row10_col4, #T_1811e_row14_col4, #T_1811e_row15_col2, #T_1811e_row24_col0 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row10_col7, #T_1811e_row14_col6 {\n",
       "  background-color: #ffb9b9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row10_col9, #T_1811e_row19_col4 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row11_col0 {\n",
       "  background-color: #9d9dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row11_col1, #T_1811e_row14_col2, #T_1811e_row16_col1 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row11_col4, #T_1811e_row20_col5 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row11_col6, #T_1811e_row12_col6, #T_1811e_row15_col9 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row12_col0 {\n",
       "  background-color: #7171ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row12_col3, #T_1811e_row13_col3, #T_1811e_row16_col2, #T_1811e_row16_col3, #T_1811e_row16_col7 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row12_col5, #T_1811e_row12_col8 {\n",
       "  background-color: #ffd9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row13_col0, #T_1811e_row18_col2 {\n",
       "  background-color: #8d8dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row13_col1, #T_1811e_row16_col0 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row13_col4, #T_1811e_row18_col4 {\n",
       "  background-color: #ff9d9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row13_col5 {\n",
       "  background-color: #ffc1c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row13_col6, #T_1811e_row17_col5 {\n",
       "  background-color: #ffadad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row13_col7, #T_1811e_row18_col7 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row13_col8, #T_1811e_row13_col9, #T_1811e_row17_col4, #T_1811e_row20_col2 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row14_col0, #T_1811e_row19_col1 {\n",
       "  background-color: #7575ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row14_col5, #T_1811e_row17_col3 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row14_col9, #T_1811e_row17_col7, #T_1811e_row21_col5 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row15_col7, #T_1811e_row18_col6, #T_1811e_row19_col6 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row16_col4 {\n",
       "  background-color: #ffb5b5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row16_col6 {\n",
       "  background-color: #ff8181;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row17_col0 {\n",
       "  background-color: #8181ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row18_col0, #T_1811e_row22_col2 {\n",
       "  background-color: #5d5dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row18_col9 {\n",
       "  background-color: #e60000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row19_col0 {\n",
       "  background-color: #6161ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row19_col7, #T_1811e_row22_col1 {\n",
       "  background-color: #ea0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row19_col9, #T_1811e_row22_col5 {\n",
       "  background-color: #ff5555;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row20_col1 {\n",
       "  background-color: #bdbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row20_col3 {\n",
       "  background-color: #a9a9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row20_col6 {\n",
       "  background-color: #ff7575;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row20_col7 {\n",
       "  background-color: #aa0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row20_col8 {\n",
       "  background-color: #f40000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row20_col9, #T_1811e_row21_col4, #T_1811e_row21_col6 {\n",
       "  background-color: #800000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row21_col0 {\n",
       "  background-color: #5151ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row21_col3 {\n",
       "  background-color: #fa0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row21_col7 {\n",
       "  background-color: #9191ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row21_col8 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1811e_row22_col3 {\n",
       "  background-color: #ec0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1811e_row22_col4 {\n",
       "  background-color: #ff2525;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1811e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_1811e_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_1811e_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_1811e_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_1811e_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_1811e_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "      <th id=\"T_1811e_level0_col5\" class=\"col_heading level0 col5\" >6</th>\n",
       "      <th id=\"T_1811e_level0_col6\" class=\"col_heading level0 col6\" >7</th>\n",
       "      <th id=\"T_1811e_level0_col7\" class=\"col_heading level0 col7\" >8</th>\n",
       "      <th id=\"T_1811e_level0_col8\" class=\"col_heading level0 col8\" >9</th>\n",
       "      <th id=\"T_1811e_level0_col9\" class=\"col_heading level0 col9\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row0\" class=\"row_heading level0 row0\" >0.360000</th>\n",
       "      <td id=\"T_1811e_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_1811e_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_1811e_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_1811e_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_1811e_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_1811e_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_1811e_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_1811e_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_1811e_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_1811e_row0_col9\" class=\"data row0 col9\" >3.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row1\" class=\"row_heading level0 row1\" >0.510000</th>\n",
       "      <td id=\"T_1811e_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_1811e_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_1811e_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_1811e_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_1811e_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_1811e_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_1811e_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_1811e_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_1811e_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_1811e_row1_col9\" class=\"data row1 col9\" >6.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row2\" class=\"row_heading level0 row2\" >0.710000</th>\n",
       "      <td id=\"T_1811e_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_1811e_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_1811e_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_1811e_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_1811e_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_1811e_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_1811e_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_1811e_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_1811e_row2_col8\" class=\"data row2 col8\" >1.07%</td>\n",
       "      <td id=\"T_1811e_row2_col9\" class=\"data row2 col9\" >1.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row3\" class=\"row_heading level0 row3\" >1.000000</th>\n",
       "      <td id=\"T_1811e_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_1811e_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_1811e_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_1811e_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_1811e_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_1811e_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_1811e_row3_col6\" class=\"data row3 col6\" >4.64%</td>\n",
       "      <td id=\"T_1811e_row3_col7\" class=\"data row3 col7\" >3.50%</td>\n",
       "      <td id=\"T_1811e_row3_col8\" class=\"data row3 col8\" >-5.69%</td>\n",
       "      <td id=\"T_1811e_row3_col9\" class=\"data row3 col9\" >-0.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row4\" class=\"row_heading level0 row4\" >1.400000</th>\n",
       "      <td id=\"T_1811e_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_1811e_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_1811e_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_1811e_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_1811e_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_1811e_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_1811e_row4_col6\" class=\"data row4 col6\" >1.82%</td>\n",
       "      <td id=\"T_1811e_row4_col7\" class=\"data row4 col7\" >2.57%</td>\n",
       "      <td id=\"T_1811e_row4_col8\" class=\"data row4 col8\" >3.09%</td>\n",
       "      <td id=\"T_1811e_row4_col9\" class=\"data row4 col9\" >-1.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row5\" class=\"row_heading level0 row5\" >1.960000</th>\n",
       "      <td id=\"T_1811e_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_1811e_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_1811e_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_1811e_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_1811e_row5_col4\" class=\"data row5 col4\" ></td>\n",
       "      <td id=\"T_1811e_row5_col5\" class=\"data row5 col5\" >10.24%</td>\n",
       "      <td id=\"T_1811e_row5_col6\" class=\"data row5 col6\" >0.90%</td>\n",
       "      <td id=\"T_1811e_row5_col7\" class=\"data row5 col7\" >-1.16%</td>\n",
       "      <td id=\"T_1811e_row5_col8\" class=\"data row5 col8\" >-3.73%</td>\n",
       "      <td id=\"T_1811e_row5_col9\" class=\"data row5 col9\" >-0.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row6\" class=\"row_heading level0 row6\" >2.740000</th>\n",
       "      <td id=\"T_1811e_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_1811e_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_1811e_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_1811e_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "      <td id=\"T_1811e_row6_col4\" class=\"data row6 col4\" ></td>\n",
       "      <td id=\"T_1811e_row6_col5\" class=\"data row6 col5\" >4.49%</td>\n",
       "      <td id=\"T_1811e_row6_col6\" class=\"data row6 col6\" >-0.46%</td>\n",
       "      <td id=\"T_1811e_row6_col7\" class=\"data row6 col7\" >0.81%</td>\n",
       "      <td id=\"T_1811e_row6_col8\" class=\"data row6 col8\" >-1.00%</td>\n",
       "      <td id=\"T_1811e_row6_col9\" class=\"data row6 col9\" >-0.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row7\" class=\"row_heading level0 row7\" >3.840000</th>\n",
       "      <td id=\"T_1811e_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_1811e_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_1811e_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_1811e_row7_col3\" class=\"data row7 col3\" ></td>\n",
       "      <td id=\"T_1811e_row7_col4\" class=\"data row7 col4\" >-2.05%</td>\n",
       "      <td id=\"T_1811e_row7_col5\" class=\"data row7 col5\" >0.52%</td>\n",
       "      <td id=\"T_1811e_row7_col6\" class=\"data row7 col6\" >-2.37%</td>\n",
       "      <td id=\"T_1811e_row7_col7\" class=\"data row7 col7\" >-0.61%</td>\n",
       "      <td id=\"T_1811e_row7_col8\" class=\"data row7 col8\" >-2.20%</td>\n",
       "      <td id=\"T_1811e_row7_col9\" class=\"data row7 col9\" >0.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row8\" class=\"row_heading level0 row8\" >5.380000</th>\n",
       "      <td id=\"T_1811e_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_1811e_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_1811e_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_1811e_row8_col3\" class=\"data row8 col3\" >-5.84%</td>\n",
       "      <td id=\"T_1811e_row8_col4\" class=\"data row8 col4\" >-0.08%</td>\n",
       "      <td id=\"T_1811e_row8_col5\" class=\"data row8 col5\" >-1.62%</td>\n",
       "      <td id=\"T_1811e_row8_col6\" class=\"data row8 col6\" >-1.77%</td>\n",
       "      <td id=\"T_1811e_row8_col7\" class=\"data row8 col7\" >1.40%</td>\n",
       "      <td id=\"T_1811e_row8_col8\" class=\"data row8 col8\" >-2.68%</td>\n",
       "      <td id=\"T_1811e_row8_col9\" class=\"data row8 col9\" >1.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row9\" class=\"row_heading level0 row9\" >7.530000</th>\n",
       "      <td id=\"T_1811e_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_1811e_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_1811e_row9_col2\" class=\"data row9 col2\" >3.33%</td>\n",
       "      <td id=\"T_1811e_row9_col3\" class=\"data row9 col3\" >1.04%</td>\n",
       "      <td id=\"T_1811e_row9_col4\" class=\"data row9 col4\" >-0.38%</td>\n",
       "      <td id=\"T_1811e_row9_col5\" class=\"data row9 col5\" >-0.49%</td>\n",
       "      <td id=\"T_1811e_row9_col6\" class=\"data row9 col6\" >1.12%</td>\n",
       "      <td id=\"T_1811e_row9_col7\" class=\"data row9 col7\" >-1.13%</td>\n",
       "      <td id=\"T_1811e_row9_col8\" class=\"data row9 col8\" >0.21%</td>\n",
       "      <td id=\"T_1811e_row9_col9\" class=\"data row9 col9\" >0.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row10\" class=\"row_heading level0 row10\" >10.540000</th>\n",
       "      <td id=\"T_1811e_row10_col0\" class=\"data row10 col0\" ></td>\n",
       "      <td id=\"T_1811e_row10_col1\" class=\"data row10 col1\" >0.79%</td>\n",
       "      <td id=\"T_1811e_row10_col2\" class=\"data row10 col2\" >-0.16%</td>\n",
       "      <td id=\"T_1811e_row10_col3\" class=\"data row10 col3\" >-1.24%</td>\n",
       "      <td id=\"T_1811e_row10_col4\" class=\"data row10 col4\" >-1.30%</td>\n",
       "      <td id=\"T_1811e_row10_col5\" class=\"data row10 col5\" >0.55%</td>\n",
       "      <td id=\"T_1811e_row10_col6\" class=\"data row10 col6\" >1.21%</td>\n",
       "      <td id=\"T_1811e_row10_col7\" class=\"data row10 col7\" >2.69%</td>\n",
       "      <td id=\"T_1811e_row10_col8\" class=\"data row10 col8\" >-0.05%</td>\n",
       "      <td id=\"T_1811e_row10_col9\" class=\"data row10 col9\" >0.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row11\" class=\"row_heading level0 row11\" >14.760000</th>\n",
       "      <td id=\"T_1811e_row11_col0\" class=\"data row11 col0\" >-3.78%</td>\n",
       "      <td id=\"T_1811e_row11_col1\" class=\"data row11 col1\" >-1.97%</td>\n",
       "      <td id=\"T_1811e_row11_col2\" class=\"data row11 col2\" >-1.85%</td>\n",
       "      <td id=\"T_1811e_row11_col3\" class=\"data row11 col3\" >-0.95%</td>\n",
       "      <td id=\"T_1811e_row11_col4\" class=\"data row11 col4\" >-1.49%</td>\n",
       "      <td id=\"T_1811e_row11_col5\" class=\"data row11 col5\" >1.38%</td>\n",
       "      <td id=\"T_1811e_row11_col6\" class=\"data row11 col6\" >2.33%</td>\n",
       "      <td id=\"T_1811e_row11_col7\" class=\"data row11 col7\" >1.79%</td>\n",
       "      <td id=\"T_1811e_row11_col8\" class=\"data row11 col8\" >1.07%</td>\n",
       "      <td id=\"T_1811e_row11_col9\" class=\"data row11 col9\" >0.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row12\" class=\"row_heading level0 row12\" >20.660000</th>\n",
       "      <td id=\"T_1811e_row12_col0\" class=\"data row12 col0\" >-5.60%</td>\n",
       "      <td id=\"T_1811e_row12_col1\" class=\"data row12 col1\" >-3.67%</td>\n",
       "      <td id=\"T_1811e_row12_col2\" class=\"data row12 col2\" >-2.18%</td>\n",
       "      <td id=\"T_1811e_row12_col3\" class=\"data row12 col3\" >-0.73%</td>\n",
       "      <td id=\"T_1811e_row12_col4\" class=\"data row12 col4\" >0.80%</td>\n",
       "      <td id=\"T_1811e_row12_col5\" class=\"data row12 col5\" >1.41%</td>\n",
       "      <td id=\"T_1811e_row12_col6\" class=\"data row12 col6\" >2.29%</td>\n",
       "      <td id=\"T_1811e_row12_col7\" class=\"data row12 col7\" >-0.08%</td>\n",
       "      <td id=\"T_1811e_row12_col8\" class=\"data row12 col8\" >1.45%</td>\n",
       "      <td id=\"T_1811e_row12_col9\" class=\"data row12 col9\" >1.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row13\" class=\"row_heading level0 row13\" >28.930000</th>\n",
       "      <td id=\"T_1811e_row13_col0\" class=\"data row13 col0\" >-4.40%</td>\n",
       "      <td id=\"T_1811e_row13_col1\" class=\"data row13 col1\" >-3.56%</td>\n",
       "      <td id=\"T_1811e_row13_col2\" class=\"data row13 col2\" >-1.61%</td>\n",
       "      <td id=\"T_1811e_row13_col3\" class=\"data row13 col3\" >-0.67%</td>\n",
       "      <td id=\"T_1811e_row13_col4\" class=\"data row13 col4\" >3.86%</td>\n",
       "      <td id=\"T_1811e_row13_col5\" class=\"data row13 col5\" >2.48%</td>\n",
       "      <td id=\"T_1811e_row13_col6\" class=\"data row13 col6\" >3.27%</td>\n",
       "      <td id=\"T_1811e_row13_col7\" class=\"data row13 col7\" >3.67%</td>\n",
       "      <td id=\"T_1811e_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "      <td id=\"T_1811e_row13_col9\" class=\"data row13 col9\" >0.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row14\" class=\"row_heading level0 row14\" >40.500000</th>\n",
       "      <td id=\"T_1811e_row14_col0\" class=\"data row14 col0\" >-5.35%</td>\n",
       "      <td id=\"T_1811e_row14_col1\" class=\"data row14 col1\" >-2.49%</td>\n",
       "      <td id=\"T_1811e_row14_col2\" class=\"data row14 col2\" >-2.02%</td>\n",
       "      <td id=\"T_1811e_row14_col3\" class=\"data row14 col3\" >1.07%</td>\n",
       "      <td id=\"T_1811e_row14_col4\" class=\"data row14 col4\" >-1.29%</td>\n",
       "      <td id=\"T_1811e_row14_col5\" class=\"data row14 col5\" >0.43%</td>\n",
       "      <td id=\"T_1811e_row14_col6\" class=\"data row14 col6\" >2.73%</td>\n",
       "      <td id=\"T_1811e_row14_col7\" class=\"data row14 col7\" >3.52%</td>\n",
       "      <td id=\"T_1811e_row14_col8\" class=\"data row14 col8\" >0.19%</td>\n",
       "      <td id=\"T_1811e_row14_col9\" class=\"data row14 col9\" >2.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row15\" class=\"row_heading level0 row15\" >56.690000</th>\n",
       "      <td id=\"T_1811e_row15_col0\" class=\"data row15 col0\" >-1.22%</td>\n",
       "      <td id=\"T_1811e_row15_col1\" class=\"data row15 col1\" >-2.25%</td>\n",
       "      <td id=\"T_1811e_row15_col2\" class=\"data row15 col2\" >-1.30%</td>\n",
       "      <td id=\"T_1811e_row15_col3\" class=\"data row15 col3\" >1.63%</td>\n",
       "      <td id=\"T_1811e_row15_col4\" class=\"data row15 col4\" >-2.80%</td>\n",
       "      <td id=\"T_1811e_row15_col5\" class=\"data row15 col5\" >1.73%</td>\n",
       "      <td id=\"T_1811e_row15_col6\" class=\"data row15 col6\" >3.32%</td>\n",
       "      <td id=\"T_1811e_row15_col7\" class=\"data row15 col7\" >1.90%</td>\n",
       "      <td id=\"T_1811e_row15_col8\" class=\"data row15 col8\" >1.64%</td>\n",
       "      <td id=\"T_1811e_row15_col9\" class=\"data row15 col9\" >2.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row16\" class=\"row_heading level0 row16\" >79.370000</th>\n",
       "      <td id=\"T_1811e_row16_col0\" class=\"data row16 col0\" >-3.49%</td>\n",
       "      <td id=\"T_1811e_row16_col1\" class=\"data row16 col1\" >-1.91%</td>\n",
       "      <td id=\"T_1811e_row16_col2\" class=\"data row16 col2\" >-0.66%</td>\n",
       "      <td id=\"T_1811e_row16_col3\" class=\"data row16 col3\" >-0.65%</td>\n",
       "      <td id=\"T_1811e_row16_col4\" class=\"data row16 col4\" >2.85%</td>\n",
       "      <td id=\"T_1811e_row16_col5\" class=\"data row16 col5\" >1.39%</td>\n",
       "      <td id=\"T_1811e_row16_col6\" class=\"data row16 col6\" >4.88%</td>\n",
       "      <td id=\"T_1811e_row16_col7\" class=\"data row16 col7\" >-0.74%</td>\n",
       "      <td id=\"T_1811e_row16_col8\" class=\"data row16 col8\" >-1.07%</td>\n",
       "      <td id=\"T_1811e_row16_col9\" class=\"data row16 col9\" >4.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row17\" class=\"row_heading level0 row17\" >111.120000</th>\n",
       "      <td id=\"T_1811e_row17_col0\" class=\"data row17 col0\" >-4.99%</td>\n",
       "      <td id=\"T_1811e_row17_col1\" class=\"data row17 col1\" >-2.69%</td>\n",
       "      <td id=\"T_1811e_row17_col2\" class=\"data row17 col2\" >-0.54%</td>\n",
       "      <td id=\"T_1811e_row17_col3\" class=\"data row17 col3\" >0.36%</td>\n",
       "      <td id=\"T_1811e_row17_col4\" class=\"data row17 col4\" >0.01%</td>\n",
       "      <td id=\"T_1811e_row17_col5\" class=\"data row17 col5\" >3.19%</td>\n",
       "      <td id=\"T_1811e_row17_col6\" class=\"data row17 col6\" >-0.21%</td>\n",
       "      <td id=\"T_1811e_row17_col7\" class=\"data row17 col7\" >2.03%</td>\n",
       "      <td id=\"T_1811e_row17_col8\" class=\"data row17 col8\" >0.17%</td>\n",
       "      <td id=\"T_1811e_row17_col9\" class=\"data row17 col9\" >1.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row18\" class=\"row_heading level0 row18\" >155.570000</th>\n",
       "      <td id=\"T_1811e_row18_col0\" class=\"data row18 col0\" >-6.30%</td>\n",
       "      <td id=\"T_1811e_row18_col1\" class=\"data row18 col1\" >-2.79%</td>\n",
       "      <td id=\"T_1811e_row18_col2\" class=\"data row18 col2\" >-4.50%</td>\n",
       "      <td id=\"T_1811e_row18_col3\" class=\"data row18 col3\" >-2.18%</td>\n",
       "      <td id=\"T_1811e_row18_col4\" class=\"data row18 col4\" >3.90%</td>\n",
       "      <td id=\"T_1811e_row18_col5\" class=\"data row18 col5\" >0.81%</td>\n",
       "      <td id=\"T_1811e_row18_col6\" class=\"data row18 col6\" >2.02%</td>\n",
       "      <td id=\"T_1811e_row18_col7\" class=\"data row18 col7\" >3.74%</td>\n",
       "      <td id=\"T_1811e_row18_col8\" class=\"data row18 col8\" >4.51%</td>\n",
       "      <td id=\"T_1811e_row18_col9\" class=\"data row18 col9\" >11.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row19\" class=\"row_heading level0 row19\" >217.800000</th>\n",
       "      <td id=\"T_1811e_row19_col0\" class=\"data row19 col0\" >-6.16%</td>\n",
       "      <td id=\"T_1811e_row19_col1\" class=\"data row19 col1\" >-5.46%</td>\n",
       "      <td id=\"T_1811e_row19_col2\" class=\"data row19 col2\" >-2.49%</td>\n",
       "      <td id=\"T_1811e_row19_col3\" class=\"data row19 col3\" >-0.91%</td>\n",
       "      <td id=\"T_1811e_row19_col4\" class=\"data row19 col4\" >0.64%</td>\n",
       "      <td id=\"T_1811e_row19_col5\" class=\"data row19 col5\" >-1.03%</td>\n",
       "      <td id=\"T_1811e_row19_col6\" class=\"data row19 col6\" >1.88%</td>\n",
       "      <td id=\"T_1811e_row19_col7\" class=\"data row19 col7\" >11.61%</td>\n",
       "      <td id=\"T_1811e_row19_col8\" class=\"data row19 col8\" >-5.66%</td>\n",
       "      <td id=\"T_1811e_row19_col9\" class=\"data row19 col9\" >6.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row20\" class=\"row_heading level0 row20\" >304.910000</th>\n",
       "      <td id=\"T_1811e_row20_col0\" class=\"data row20 col0\" >3.12%</td>\n",
       "      <td id=\"T_1811e_row20_col1\" class=\"data row20 col1\" >-2.62%</td>\n",
       "      <td id=\"T_1811e_row20_col2\" class=\"data row20 col2\" >0.04%</td>\n",
       "      <td id=\"T_1811e_row20_col3\" class=\"data row20 col3\" >-3.37%</td>\n",
       "      <td id=\"T_1811e_row20_col4\" class=\"data row20 col4\" >-2.72%</td>\n",
       "      <td id=\"T_1811e_row20_col5\" class=\"data row20 col5\" >-1.54%</td>\n",
       "      <td id=\"T_1811e_row20_col6\" class=\"data row20 col6\" >5.43%</td>\n",
       "      <td id=\"T_1811e_row20_col7\" class=\"data row20 col7\" >16.69%</td>\n",
       "      <td id=\"T_1811e_row20_col8\" class=\"data row20 col8\" >10.90%</td>\n",
       "      <td id=\"T_1811e_row20_col9\" class=\"data row20 col9\" >96.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row21\" class=\"row_heading level0 row21\" >426.880000</th>\n",
       "      <td id=\"T_1811e_row21_col0\" class=\"data row21 col0\" >-6.85%</td>\n",
       "      <td id=\"T_1811e_row21_col1\" class=\"data row21 col1\" >4.40%</td>\n",
       "      <td id=\"T_1811e_row21_col2\" class=\"data row21 col2\" >-0.46%</td>\n",
       "      <td id=\"T_1811e_row21_col3\" class=\"data row21 col3\" >10.39%</td>\n",
       "      <td id=\"T_1811e_row21_col4\" class=\"data row21 col4\" >35.96%</td>\n",
       "      <td id=\"T_1811e_row21_col5\" class=\"data row21 col5\" >2.06%</td>\n",
       "      <td id=\"T_1811e_row21_col6\" class=\"data row21 col6\" >30.56%</td>\n",
       "      <td id=\"T_1811e_row21_col7\" class=\"data row21 col7\" >-4.23%</td>\n",
       "      <td id=\"T_1811e_row21_col8\" class=\"data row21 col8\" >-3.06%</td>\n",
       "      <td id=\"T_1811e_row21_col9\" class=\"data row21 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row22\" class=\"row_heading level0 row22\" >597.630000</th>\n",
       "      <td id=\"T_1811e_row22_col0\" class=\"data row22 col0\" ></td>\n",
       "      <td id=\"T_1811e_row22_col1\" class=\"data row22 col1\" >11.70%</td>\n",
       "      <td id=\"T_1811e_row22_col2\" class=\"data row22 col2\" >-6.39%</td>\n",
       "      <td id=\"T_1811e_row22_col3\" class=\"data row22 col3\" >11.48%</td>\n",
       "      <td id=\"T_1811e_row22_col4\" class=\"data row22 col4\" >8.58%</td>\n",
       "      <td id=\"T_1811e_row22_col5\" class=\"data row22 col5\" >6.68%</td>\n",
       "      <td id=\"T_1811e_row22_col6\" class=\"data row22 col6\" >-0.96%</td>\n",
       "      <td id=\"T_1811e_row22_col7\" class=\"data row22 col7\" ></td>\n",
       "      <td id=\"T_1811e_row22_col8\" class=\"data row22 col8\" ></td>\n",
       "      <td id=\"T_1811e_row22_col9\" class=\"data row22 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row23\" class=\"row_heading level0 row23\" >836.680000</th>\n",
       "      <td id=\"T_1811e_row23_col0\" class=\"data row23 col0\" >-0.26%</td>\n",
       "      <td id=\"T_1811e_row23_col1\" class=\"data row23 col1\" ></td>\n",
       "      <td id=\"T_1811e_row23_col2\" class=\"data row23 col2\" >-0.14%</td>\n",
       "      <td id=\"T_1811e_row23_col3\" class=\"data row23 col3\" ></td>\n",
       "      <td id=\"T_1811e_row23_col4\" class=\"data row23 col4\" ></td>\n",
       "      <td id=\"T_1811e_row23_col5\" class=\"data row23 col5\" ></td>\n",
       "      <td id=\"T_1811e_row23_col6\" class=\"data row23 col6\" ></td>\n",
       "      <td id=\"T_1811e_row23_col7\" class=\"data row23 col7\" ></td>\n",
       "      <td id=\"T_1811e_row23_col8\" class=\"data row23 col8\" ></td>\n",
       "      <td id=\"T_1811e_row23_col9\" class=\"data row23 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1811e_level0_row24\" class=\"row_heading level0 row24\" >1171.360000</th>\n",
       "      <td id=\"T_1811e_row24_col0\" class=\"data row24 col0\" >-1.28%</td>\n",
       "      <td id=\"T_1811e_row24_col1\" class=\"data row24 col1\" ></td>\n",
       "      <td id=\"T_1811e_row24_col2\" class=\"data row24 col2\" ></td>\n",
       "      <td id=\"T_1811e_row24_col3\" class=\"data row24 col3\" ></td>\n",
       "      <td id=\"T_1811e_row24_col4\" class=\"data row24 col4\" ></td>\n",
       "      <td id=\"T_1811e_row24_col5\" class=\"data row24 col5\" ></td>\n",
       "      <td id=\"T_1811e_row24_col6\" class=\"data row24 col6\" ></td>\n",
       "      <td id=\"T_1811e_row24_col7\" class=\"data row24 col7\" ></td>\n",
       "      <td id=\"T_1811e_row24_col8\" class=\"data row24 col8\" ></td>\n",
       "      <td id=\"T_1811e_row24_col9\" class=\"data row24 col9\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2ba30fd90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "B_W_Metric_pivot = B_W_Metric.pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='d_bin'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "B_W_Metric_raw.groupby(by=['d_bin']).agg('count').reset_index().plot.bar(x='d_bin', y='p', legend=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
